def save_experiment(self):
with open(f"./output/{self._experiment._id}.ckp", "wb") as fh:
pickle.dump(self._agent, fh)
neptune.log_artifact(f"./output/{self._experiment._id}.ckp")
neptune.append_tag(self._args.agent)
neptune.append_tag(self._args.environment)
def main(args):
init_logger()
set_seed(args)
if args.logger:
neptune.init("wjdghks950/NumericHGN")
neptune.create_experiment(name="({}) NumHGN_{}_{}_{}".format(
args.task, args.train_batch_size, args.max_seq_len,
args.train_file))
neptune.append_tag("BertForSequenceClassification", "finetuning",
"num_augmented_HGN")
tokenizer = load_tokenizer(args)
train_dataset = dev_dataset = test_dataset = None
if args.do_train:
train_dataset = load_and_cache_examples(args, tokenizer, mode="train")
dev_dataset = load_and_cache_examples(args, tokenizer, mode="dev")
# test_dataset = load_and_cache_examples(args, tokenizer, mode="test")
trainer = ParaSelectorTrainer(args, train_dataset, dev_dataset)
if args.do_train:
trainer.train()
trainer.save_model()
if args.do_eval:
trainer.load_model()
trainer.evaluate("dev")
if args.logger:
neptune.stop()
def write_results(config: configure_finetuning.FinetuningConfig, results):
"""Write evaluation metrics to disk."""
utils.log("Writing results to", config.results_txt)
utils.mkdir(config.results_txt.rsplit("/", 1)[0])
utils.write_pickle(results, config.results_pkl)
with tf.io.gfile.GFile(config.results_txt, "a") as f:
results_str = ""
for trial_results in results:
for task_name, task_results in trial_results.items():
if task_name == "time" or task_name == "global_step":
continue
results_str += task_name + ": " + " - ".join([
"{:}: {:.2f}".format(k, v)
for k, v in task_results.items()
]) + "\n"
# Neptune Metric Logging
neptune.append_tag('ft')
neptune.append_tag('tensorflow')
neptune.set_property('task', task_name)
for k, v in task_results.items():
neptune.log_metric(k, v)
f.write(results_str)
utils.write_pickle(results, config.results_pkl)
def modify_tags(self):
neptune.append_tags("tag1")
neptune.append_tag(["tag2_to_remove", "tag3"])
neptune.remove_tag("tag2_to_remove")
neptune.remove_tag("tag4_remove_non_existing")
exp = neptune.get_experiment()
assert set(exp.get_tags()) == {
"initial tag 1", "initial tag 2", "tag1", "tag3"
}
def train(cfg, network):
if cfg.train.dataset[:4] != 'City':
torch.multiprocessing.set_sharing_strategy('file_system')
trainer = make_trainer(cfg, network)
optimizer = make_optimizer(cfg, network)
scheduler = make_lr_scheduler(cfg, optimizer)
recorder = make_recorder(cfg)
if 'Coco' not in cfg.train.dataset:
evaluator = make_evaluator(cfg)
begin_epoch = load_model(network,
optimizer,
scheduler,
recorder,
cfg.model_dir,
resume=cfg.resume)
# set_lr_scheduler(cfg, scheduler)
train_loader = make_data_loader(cfg, is_train=True)
val_loader = make_data_loader(cfg, is_train=False)
# train_loader = make_data_loader(cfg, is_train=True, max_iter=100)
global_steps = None
if cfg.neptune:
global_steps = {
'train_global_steps': 0,
'valid_global_steps': 0,
}
neptune.init('hccccccccc/clean-pvnet')
neptune.create_experiment(cfg.model_dir.split('/')[-1])
neptune.append_tag('pose')
for epoch in range(begin_epoch, cfg.train.epoch):
recorder.epoch = epoch
trainer.train(epoch, train_loader, optimizer, recorder, global_steps)
scheduler.step()
if (epoch + 1) % cfg.save_ep == 0:
save_model(network, optimizer, scheduler, recorder, epoch,
cfg.model_dir)
if (epoch + 1) % cfg.eval_ep == 0:
if 'Coco' in cfg.train.dataset:
trainer.val_coco(val_loader, global_steps)
else:
trainer.val(epoch, val_loader, evaluator, recorder)
if cfg.neptune:
neptune.stop()
return network
def __init__(self, learning_rate):
neptune.init(
api_token="eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vdWkubmVwdHVuZS5tbCIsImFwaV9rZXkiOiJjYjdhMGI5Ny02YTNmLTRlN2MtOTkyYi1jNDM0YjRmMjM5MDQifQ==",
project_qualified_name="martinjms/examples",
)
neptune.create_experiment(
name="caisim-example", params=dict(learning_rate=learning_rate)
)
neptune.append_tag("minimal-example")
self.learning_rate = learning_rate
# Then pytorch stuff..
self.lin = torch.nn.Linear(50, 10)
self.opt = torch.optim.SGD(self.lin.parameters(), learning_rate)
self.step = 0
self.lin.to(device)
def add_tags(self, tags):
'''
Adds parameters to experiment log
Parameters
----------
params : tags
list of tags (strings)
e.g.: ['tag1', 'tag2']
Returns
-------
None.
'''
if self.neptune:
neptune.append_tag(tags)
if self.comet:
self.comet_experiment.add_tags(tags)
def run_roshambo():
seed = 0x1B
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
neptune.set_property("seed", seed)
neptune.append_tag("ROSHAMBO")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
_logger.info("Using device type %s", str(device))
reduction_factor = 5 # Reduce dimension axis by this factor
neptune.set_property("reduction_factor", reduction_factor)
width = 240 // reduction_factor
height = 180 // reduction_factor
n_features = width * height * 2
batch_size = 5
neptune.set_property("batch_size", batch_size)
dt = 1 * ms
neptune.set_property("dt", dt)
bin_size = 50 * ms
neptune.set_property("bin_size", bin_size)
bin_steps = rescale(bin_size, dt, int)
duration_per_sample = 500 * ms
neptune.set_property("duration_per_sample", duration_per_sample)
number_of_steps = rescale(duration_per_sample, dt, int)
topology = SmallWorldTopology(
SmallWorldTopology.Configuration(
minicolumn_shape=(7, 7, 7),
macrocolumn_shape=(3, 3, 3),
minicolumn_spacing=300,
p_max=0.025,
sparse_init=True,
)
)
n_neurons = topology.number_of_nodes()
nb_of_bins = 1 + number_of_steps // bin_steps
linear_readout = LinearWithBN(n_neurons * nb_of_bins, 3).to(device)
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(linear_readout.parameters(), lr=0.001)
neptune.set_property("adam.lr", 0.001)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.1)
neptune.set_property("steplr.gamma", 0.1)
neptune.set_property("steplr.step_size", 2)
p_critical_configs = {
"alpha": 0.0025,
"beta": 0.00025,
"tau_v": 50 * ms,
"tau_i": 5 * ms,
"v_th": 1.0,
}
for k, v in p_critical_configs.items():
neptune.set_property(k, v)
model = PCritical(
n_features, batch_size, topology, dt=dt, **p_critical_configs,
).to(device)
all_transforms = Compose(
[
ScaleDown(240, 180, factor=reduction_factor),
ToDense(width, height, duration_per_sample, dt=dt),
Flatten(),
]
)
label_dict = {
"scissors": 0,
"paper": 1,
"rock": 2,
}
data = INIRoshambo(
os.getenv("ROSHAMBO_DATASET_LOCATION_500ms_subsamples"),
transforms=all_transforms,
)
train_data, val_data = split_per_user(data, train_ratio=0.85)
_logger.info(
"Keeping %i samples for training and %i for validation",
len(train_data),
len(val_data),
)
def labels_to_tensor(labels):
return torch.tensor([label_dict[l] for l in labels])
def run_batch(X, y):
current_batch_size = len(y)
model.batch_size = current_batch_size
bins = torch.zeros(current_batch_size, n_neurons, nb_of_bins, device=device)
for t in range(number_of_steps):
out_spikes = model.forward(X[:, :, t])
bins[:, :, t // bin_steps] += out_spikes
return bins
for iter_nb in range(10):
train_generator = torch_data.DataLoader(
train_data,
batch_size=batch_size,
shuffle=True,
num_workers=2,
pin_memory=True,
timeout=120,
)
for i, (X, labels) in enumerate(tqdm(train_generator)):
if i >= 20:
break
neptune.log_metric("iteration", i)
X, y = X.to(device), labels_to_tensor(labels).to(device)
# fig, axs = plt.subplots()
# display_spike_train(axs, X[0])
# plt.show()
# print(X.shape)
# exit(0)
bins = run_batch(X, y)
# fig, axs = plt.subplots()
# activity = bins[0].sum(dim=0)
# axs.plot(np.arange(nb_of_bins), activity.cpu().numpy())
# plt.show()
optimizer.zero_grad()
out = linear_readout(bins.view(len(y), -1))
loss = loss_fn(out, y)
loss.backward()
optimizer.step()
loss_val = loss.cpu().detach().item()
_logger.info("Loss: %.3f", loss_val)
neptune.log_metric("loss", loss_val)
total_accurate = 0
total_elems = 0
val_generator = torch_data.DataLoader(
val_data,
batch_size=batch_size,
shuffle=False,
num_workers=2,
pin_memory=True,
timeout=120,
)
for i, (X, labels) in enumerate(tqdm(val_generator)):
if i >= 10:
break
X, y = X.to(device), labels_to_tensor(labels).to(device)
bins = run_batch(X, y)
out = linear_readout(bins.view(len(y), -1))
preds = torch.argmax(out, dim=1)
total_accurate += torch.sum(preds == y).cpu().float().item()
total_elems += len(y)
_logger.info("Current accuracy: %.4f", total_accurate / total_elems)
neptune.log_metric("current_accuracy", total_accurate / total_elems)
scheduler.step()
_logger.info(
"Final accuracy at iter %i: %.4f", iter_nb, total_accurate / total_elems
)
neptune.log_metric("final_accuracy", total_accurate / total_elems)
def record_eval_metric(neptune, metrics):
for k, v in metrics.items():
neptune.log_metric(k, v)
# %%
model_path = '/workspace/ml-workspace/thesis_git/thesis/models/'
best_eval_f1 = 0
# Measure the total training time for the whole run.
total_t0 = time.time()
with neptune.create_experiment(name="HierarchicalSemanticGraphNetwork",
params=PARAMS,
upload_source_files=['HSGN_GAT.py']):
neptune.append_tag(
["homogeneous_graph", "GATConv", "bidirectional_token_node_edge"])
neptune.set_property('server', 'IRGPU2')
neptune.set_property('training_set_path', training_path)
neptune.set_property('dev_set_path', dev_path)
# For each epoch...
for epoch_i in range(0, epochs):
# ========================================
# Training
# ========================================
# Perform one full pass over the training set.
print("")
print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
print('Training...')
def main(**kwargs):
import sys
for k, v in kwargs.items():
sys.argv += [k, v]
from pprint import pprint
import argparse
import datetime
import json
import os
parser = argparse.ArgumentParser()
parser.add_argument('--neptune_project_name', default='jacobarose/sandbox', type=str, help='Neptune.ai project name to log under')
parser.add_argument('--experiment_name', default='pnas_minimal_example', type=str, help='Neptune.ai experiment name to log under')
parser.add_argument('--config_path', default=r'/home/jacob/projects/pyleaves/pyleaves/configs/example_configs/pnas_resnet_config.json', type=str, help='JSON config file')
parser.add_argument('-gpu', '--gpu_id', default='1', type=str, help='integer number of gpu to train on', dest='gpu_id')
parser.add_argument('-tags', '--add-tags', default=[], type=str, nargs='*', help='Add arbitrary list of tags to apply to this run in neptune', dest='tags')
parser.add_argument('-f', default=None)
args = parser.parse_args()
with open(args.config_path, 'r') as config_file:
PARAMS = json.load(config_file)
# print(gpu)
# os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id)
pprint(PARAMS)
import tensorflow as tf
import neptune
# tf.debugging.set_log_device_placement(True)
print(tf.__version__)
import arrow
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import io
from stuf import stuf
from more_itertools import unzip
from functools import partial
# import tensorflow as tf
# tf.compat.v1.enable_eager_execution()
AUTOTUNE = tf.data.experimental.AUTOTUNE
from pyleaves.leavesdb.tf_utils.tf_utils import set_random_seed, reset_keras_session
import pyleaves
from pyleaves.utils.img_utils import random_pad_image
from pyleaves.utils.utils import ensure_dir_exists
from pyleaves.datasets import leaves_dataset, fossil_dataset, pnas_dataset, base_dataset
from pyleaves.models.vgg16 import VGG16, VGG16GrayScale
from pyleaves.models import resnet, vgg16
from tensorflow.compat.v1.keras.callbacks import Callback, ModelCheckpoint, TensorBoard, LearningRateScheduler, EarlyStopping
from tensorflow.keras import metrics
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras import layers
from tensorflow.keras import backend as K
import tensorflow_datasets as tfds
import neptune_tensorboard as neptune_tb
seed = 346
# set_random_seed(seed)
# reset_keras_session()
def get_preprocessing_func(model_name):
if model_name.startswith('resnet'):
from tensorflow.keras.applications.resnet_v2 import preprocess_input
elif model_name == 'vgg16':
from tensorflow.keras.applications.vgg16 import preprocess_input
elif model_name=='shallow':
def preprocess_input(x):
return x/255.0 # ((x/255.0)-0.5)*2.0
return preprocess_input #lambda x,y: (preprocess_input(x),y)
def _load_img(image_path):#, img_size=(224,224)):
img = tf.io.read_file(image_path)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
return img
# return tf.compat.v1.image.resize_image_with_pad(img, *img_size)
def _encode_label(label, num_classes=19):
label = tf.cast(label, tf.int32)
label = tf.one_hot(label, depth=num_classes)
return label
def _load_example(image_path, label, num_classes=19):
img = _load_img(image_path)
one_hot_label = _encode_label(label, num_classes=num_classes)
return img, one_hot_label
def _load_uint8_example(image_path, label, num_classes=19):
img = tf.image.convert_image_dtype(_load_img(image_path)*255.0, dtype=tf.uint8)
one_hot_label = _encode_label(label, num_classes=num_classes)
return img, one_hot_label
def rgb2gray_3channel(img, label):
'''
Convert rgb image to grayscale, but keep num_channels=3
'''
img = tf.image.rgb_to_grayscale(img)
img = tf.image.grayscale_to_rgb(img)
return img, label
def rgb2gray_1channel(img, label):
'''
Convert rgb image to grayscale, num_channels from 3 to 1
'''
img = tf.image.rgb_to_grayscale(img)
return img, label
def log_data(logs):
for k, v in logs.items():
neptune.log_metric(k, v)
neptune_logger = tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, logs: log_data(logs))
def focal_loss(gamma=2.0, alpha=4.0):
gamma = float(gamma)
alpha = float(alpha)
def focal_loss_fixed(y_true, y_pred):
"""Focal loss for multi-classification
FL(p_t)=-alpha(1-p_t)^{gamma}ln(p_t)
Notice: y_pred is probability after softmax
gradient is d(Fl)/d(p_t) not d(Fl)/d(x) as described in paper
d(Fl)/d(p_t) * [p_t(1-p_t)] = d(Fl)/d(x)
Focal Loss for Dense Object Detection
https://arxiv.org/abs/1708.02002
Arguments:
y_true {tensor} -- ground truth labels, shape of [batch_size, num_cls]
y_pred {tensor} -- model's output, shape of [batch_size, num_cls]
Keyword Arguments:
gamma {float} -- (default: {2.0})
alpha {float} -- (default: {4.0})
Returns:
[tensor] -- loss.
"""
epsilon = 1.e-9
y_true = tf.convert_to_tensor(y_true, tf.float32)
y_pred = tf.convert_to_tensor(y_pred, tf.float32)
model_out = tf.add(y_pred, epsilon)
ce = tf.multiply(y_true, -tf.log(model_out))
weight = tf.multiply(y_true, tf.pow(tf.subtract(1., model_out), gamma))
fl = tf.multiply(alpha, tf.multiply(weight, ce))
reduced_fl = tf.reduce_max(fl, axis=1)
return tf.reduce_mean(reduced_fl)
return focal_loss_fixed
def per_class_accuracy(y_true, y_pred):
return tf.metrics.mean_per_class_accuracy(y_true, y_pred, num_classes=PARAMS['num_classes'])
def build_model(model_params,
optimizer,
loss,
METRICS):
if model_params['name']=='vgg16':
model_builder = vgg16.VGG16GrayScale(model_params)
elif model_params['name'].startswith('resnet'):
model_builder = resnet.ResNet(model_params)
base = model_builder.build_base()
model = model_builder.build_head(base)
model.compile(optimizer=optimizer,
loss=loss,
metrics=METRICS)
return model
def build_shallow(input_shape=(224,224,3),
num_classes=10,
optimizer=None,
loss=None,
METRICS=None):
model = tf.keras.models.Sequential()
model.add(layers.Conv2D(64, (7, 7), activation='relu', input_shape=input_shape, kernel_initializer=tf.initializers.GlorotNormal()))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (7, 7), activation='relu', kernel_initializer=tf.initializers.GlorotNormal()))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (7, 7), activation='relu', kernel_initializer=tf.initializers.GlorotNormal()))
model.add(layers.Flatten())
model.add(layers.Dense(64*2, activation='relu', kernel_initializer=tf.initializers.GlorotNormal()))
model.add(layers.Dense(num_classes,activation='softmax', kernel_initializer=tf.initializers.GlorotNormal()))
model.compile(optimizer=optimizer,
loss=loss,
metrics=METRICS)
return model
class ImageLogger:
'''Tensorflow 2.0 version'''
def __init__(self, log_dir: str, max_images: int, name: str):
self.file_writer = tf.summary.create_file_writer(log_dir)
self.log_dir = log_dir
self.max_images = max_images
self.name = name
self._counter = tf.Variable(0, dtype=tf.int64)
self.filepaths = []
def add_log(self, img, counter=None, name=None):
'''
Intention is to generalize this to an abstract class for logging to any experiment management platform (e.g. neptune, mlflow, etc)
Currently takes a filepath pointing to an image file and logs to current neptune experiment.
'''
# scaled_images = (img - tf.math.reduce_min(img))/(tf.math.reduce_max(img) - tf.math.reduce_min(img))
# keep = 0
# scaled_images = tf.image.convert_image_dtype(tf.squeeze(scaled_images[keep,:,:,:]), dtype=tf.uint8)
# scaled_images = tf.expand_dims(scaled_images, 0)
# tf.summary.image(name=self.name, data=scaled_images, step=self._counter, max_outputs=self.max_images)
scaled_img = (img - np.min(img))/(np.max(img) - np.min(img)) * 255.0
scaled_img = scaled_img.astype(np.uint32)
neptune.log_image(log_name= name or self.name,
x=counter,
y=scaled_img)
return scaled_img
def __call__(self, images, labels):
with self.file_writer.as_default():
scaled_images = (images - tf.math.reduce_min(images))/(tf.math.reduce_max(images) - tf.math.reduce_min(images))
keep = 0
scaled_images = tf.image.convert_image_dtype(tf.squeeze(scaled_images[keep,:,:,:]), dtype=tf.uint8)
scaled_images = tf.expand_dims(scaled_images, 0)
labels = tf.argmax(labels[[keep], :],axis=1)
tf.summary.image(name=self.name, data=scaled_images, step=self._counter, max_outputs=self.max_images)
filepath = os.path.join(self.log_dir,'sample_images',f'{self.name}-{self._counter}.jpg')
scaled_images = tf.image.encode_jpeg(tf.squeeze(scaled_images))
tf.io.write_file(filename=tf.constant(filepath),
contents=scaled_images)
# self.add_log(scaled_images)
self._counter.assign_add(1)
return images, labels
def _cond_apply(x, y, func, prob):
"""Conditionally apply func to x and y with probability prob.
Parameters
----------
x : type
Input to conditionally pass through func
y : type
Label
func : type
Function to conditionally be applied to x and y
prob : type
Probability of applying function, within range [0.0,1.0]
Returns
-------
x, y
"""
return tf.cond((tf.random.uniform([], 0, 1) >= (1.0 - prob)), lambda: func(x,y), lambda: (x,y))
class ImageAugmentor:
"""Short summary.
Parameters
----------
augmentations : dict
Maps a sequence of named augmentations to a scalar probability,
according to which they'll be conditionally applied in order.
resize_w_pad : tuple, default=None
Description of parameter `resize_w_pad`.
random_crop : tuple, default=None
Description of parameter `random_crop`.
random_jitter : dict
First applies resize_w_pad, then random_crop. If user desires only 1 of these, set this to None.
Should be a dict with 2 keys:
'resize':(height, width)
'crop_size':(crop_height,crop_width, channels)
Only 1 of these 3 kwargs should be provided to any given augmentor:
{'resize_w_pad', 'random_crop', 'random_jitter'}
Example values for each:
resize_w_pad=(224,224)
random_crop=(224,224,3)
random_jitter={'resize':(338,338),
'crop_size':(224,224, 3)}
seed : int, default=None
Random seed to apply to all augmentations
Examples
-------
Examples should be written in doctest format, and
should illustrate how to use the function/class.
>>>
Attributes
----------
augmentations
"""
def __init__(self,
name='',
augmentations={'rotate':1.0,
'flip':1.0,
'color':1.0,
'rgb2gray_3channel':1.0},
resize_w_pad=None,
random_crop=None,
random_jitter={'resize':(338,338),
'crop_size':(224,224,3)},
log_dir=None,
seed=None):
self.name = name
self.augmentations = augmentations
self.seed = seed
if resize_w_pad:
self.target_h = resize_w_pad[0]
self.target_w = resize_w_pad[1]
# self.resize = self.resize_w_pad
elif random_crop:
self.crop_size = random_crop
self.target_h = self.crop_size[0]
self.target_w = self.crop_size[1]
# self.resize = self.random_crop
elif random_jitter:
# self.target_h = tf.random.uniform([], random_jitter['crop_size'][0], random_jitter['resize'][0], dtype=tf.int32, seed=self.seed)
# self.target_w = tf.random.uniform([], random_jitter['crop_size'][1], random_jitter['resize'][1], dtype=tf.int32, seed=self.seed)
self.crop_size = random_jitter['crop_size']
# self.resize = self.random_jitter
self.target_h = random_jitter['crop_size'][0]
self.target_w = random_jitter['crop_size'][1]
self.resize = self.resize_w_pad
self.maps = {'rotate':self.rotate,
'flip':self.flip,
'color':self.color,
'rgb2gray_3channel':self.rgb2gray_3channel,
'rgb2gray_1channel':self.rgb2gray_1channel}
self.log_dir = log_dir
def rotate(self, x: tf.Tensor, label: tf.Tensor) -> tf.Tensor:
"""Rotation augmentation
Args:
x, tf.Tensor: Image
label, tf.Tensor: arbitrary tensor, passes through unchanged
Returns:
Augmented image, label
"""
# Rotate 0, 90, 180, 270 degrees
return tf.image.rot90(x, tf.random.uniform(shape=[], minval=0, maxval=4, dtype=tf.int32,seed=self.seed)), label
def flip(self, x: tf.Tensor, label: tf.Tensor) -> tf.Tensor:
"""Flip augmentation
Args:
x, tf.Tensor: Image to flip
label, tf.Tensor: arbitrary tensor, passes through unchanged
Returns:
Augmented image, label
"""
x = tf.image.random_flip_left_right(x, seed=self.seed)
x = tf.image.random_flip_up_down(x, seed=self.seed)
return x, label
def color(self, x: tf.Tensor, label: tf.Tensor) -> tf.Tensor:
"""Color augmentation
Args:
x, tf.Tensor: Image
label, tf.Tensor: arbitrary tensor, passes through unchanged
Returns:
Augmented image, label
"""
x = tf.image.random_hue(x, 0.08, seed=self.seed)
x = tf.image.random_saturation(x, 0.6, 1.6, seed=self.seed)
x = tf.image.random_brightness(x, 0.05, seed=self.seed)
x = tf.image.random_contrast(x, 0.7, 1.3, seed=self.seed)
return x, label
def rgb2gray_3channel(self, x: tf.Tensor, label: tf.Tensor) -> tf.Tensor:
"""Convert RGB image -> grayscale image, maintain number of channels = 3
Args:
x, tf.Tensor: Image
label, tf.Tensor: arbitrary tensor, passes through unchanged
Returns:
Augmented image, label
"""
x = tf.image.rgb_to_grayscale(x)
x = tf.image.grayscale_to_rgb(x)
return x, label
def rgb2gray_1channel(self, x: tf.Tensor, label: tf.Tensor) -> tf.Tensor:
"""Convert RGB image -> grayscale image, reduce number of channels from 3 -> 1
Args:
x, tf.Tensor: Image
label, tf.Tensor: arbitrary tensor, passes through unchanged
Returns:
Augmented image, label
"""
x = tf.image.rgb_to_grayscale(x)
return x, label
def resize_w_pad(self, x: tf.Tensor, label: tf.Tensor) -> tf.Tensor:
# TODO Finish this
# random_pad_image(x,min_image_size=None,max_image_size=None,pad_color=None,seed=self.seed)
return tf.image.resize_with_pad(x, target_height=self.target_h, target_width=self.target_w), label
def random_crop(self, x: tf.Tensor, label: tf.Tensor) -> tf.Tensor:
return tf.image.random_crop(x, size=self.crop_size), label
@tf.function
def random_jitter(self, x: tf.Tensor, label: tf.Tensor) -> tf.Tensor:
x, label = self.resize_w_pad(x, label)
x, label = self.random_crop(x, label)
return x, label
def apply_augmentations(self, dataset: tf.data.Dataset):
"""
Call this function to apply all of the augmentation in the order of specification
provided to the constructor __init__() of ImageAugmentor.
Args:
dataset, tf.data.Dataset: must yield individual examples of form (x, y)
Returns:
Augmented dataset
"""
dataset = dataset.map(self.resize, num_parallel_calls=AUTOTUNE)
for aug_name, aug_p in self.augmentations.items():
aug = self.maps[aug_name]
dataset = dataset.map(lambda x,y: _cond_apply(x, y, aug, prob=aug_p), num_parallel_calls=AUTOTUNE)
# dataset = dataset.map(lambda x,y: _cond_apply(x, y, func=aug, prob=aug_p), num_parallel_calls=AUTOTUNE)
return dataset
class ImageLoggerCallback(Callback):
'''Tensorflow 2.0 version
Callback that keeps track of a tf.data.Dataset and logs the correct batch to neptune based on the current batch.
'''
def __init__(self, data :tf.data.Dataset, freq=1, max_images=-1, name='', encoder=None):
self.data = data
self.freq = freq
self.max_images = max_images
self.name = name
self.encoder=encoder
self.init_iterator()
def init_iterator(self):
self.data_iter = iter(self.data)
self._batch = 0
self._count = 0
self.finished = False
def yield_batch(self):
batch_data = next(self.data_iter)
self._batch += 1
self._count += batch_data[0].shape[0]
return batch_data
def add_log(self, img, counter=None, name=None):
'''
Intention is to generalize this to an abstract class for logging to any experiment management platform (e.g. neptune, mlflow, etc)
Currently takes a filepath pointing to an image file and logs to current neptune experiment.
'''
scaled_img = (img - np.min(img))/(np.max(img) - np.min(img)) * 255.0
scaled_img = scaled_img.astype(np.uint32)
neptune.log_image(log_name= name or self.name,
x=counter,
y=scaled_img)
return scaled_img
def on_train_batch_begin(self, batch, logs=None):
if batch % self.freq or self.finished:
return
while batch >= self._batch:
x, y = self.yield_batch()
if self.max_images==-1:
self.max_images=x.shape[0]
if x.ndim==3:
np.newaxis(x, axis=0)
if x.shape[0]>self.max_images:
x = x[:self.max_images,...]
y = y[:self.max_images,...]
x = x.numpy()
y = np.argmax(y.numpy(),axis=1)
if self.encoder:
y = self.encoder.decode(y)
for i in range(x.shape[0]):
# self.add_log(x[i,...], counter=i, name = f'{self.name}-{y[i]}-batch_{str(self._batch).zfill(3)}')
self.add_log(x[i,...], counter=self._count+i, name = f'{self.name}-{y[i]}')
print(f'Batch {self._batch}: Logged {np.max([x.shape[0],self.max_images])} {self.name} images to neptune')
def on_epoch_end(self, epoch, logs={}):
self.finished = True
class ConfusionMatrixCallback(Callback):
'''Tensorflow 2.0 version'''
def __init__(self, log_dir, imgs : dict, labels : dict, classes, freq=1, include_train=False, seed=None):
self.file_writer = tf.summary.create_file_writer(log_dir)
self.log_dir = log_dir
self.seed = seed
self._counter = 0
assert np.all(np.array(imgs.keys()) == np.array(labels.keys()))
self.imgs = imgs
for k,v in labels.items():
if v.ndim==2:
labels[k] = tf.argmax(v,axis=-1)
self.labels = labels
self.num_samples = {k:l.numpy().shape[0] for k,l in labels.items()}
self.classes = classes
self.freq = freq
self.include_train = include_train
def log_confusion_matrix(self, model, imgs, labels, epoch, name='', norm_cm=False):
pred_labels = model.predict_classes(imgs)
# pred_labels = tf.argmax(pred_labels,axis=-1)
pred_labels = pred_labels[:,None]
con_mat = tf.math.confusion_matrix(labels=labels, predictions=pred_labels, num_classes=len(self.classes)).numpy()
if norm_cm:
con_mat = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)
con_mat_df = pd.DataFrame(con_mat,
index = self.classes,
columns = self.classes)
figure = plt.figure(figsize=(12, 12))
sns.heatmap(con_mat_df, annot=True, cmap=plt.cm.Blues)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
image = tf.image.decode_png(buf.getvalue(), channels=4)
image = tf.expand_dims(image, 0)
with self.file_writer.as_default():
tf.summary.image(name=name+'_confusion_matrix', data=image, step=self._counter)
neptune.log_image(log_name=name+'_confusion_matrix',
x=self._counter,
y=figure)
plt.close(figure)
self._counter += 1
return image
def on_epoch_end(self, epoch, logs={}):
if (not self.freq) or (epoch%self.freq != 0):
return
if self.include_train:
cm_summary_image = self.log_confusion_matrix(self.model, self.imgs['train'], self.labels['train'], epoch=epoch, name='train')
cm_summary_image = self.log_confusion_matrix(self.model, self.imgs['val'], self.labels['val'], epoch=epoch, name='val')
####################################################################################
####################################################################################
####################################################################################
neptune.init(project_qualified_name=args.neptune_project_name)
# neptune_tb.integrate_with_tensorflow()
experiment_dir = '/media/data/jacob/sandbox_logs'
experiment_name = args.experiment_name
experiment_start_time = arrow.utcnow().format('YYYY-MM-DD_HH-mm-ss')
log_dir =os.path.join(experiment_dir, experiment_name, 'log_dir',PARAMS['loss'], experiment_start_time)
ensure_dir_exists(log_dir)
print('Tensorboard log_dir: ', log_dir)
# os.system(f'neptune tensorboard {log_dir} --project {args.neptune_project_name}')
weights_best = os.path.join(log_dir, 'model_ckpt.h5')
restore_best_weights=False
histogram_freq=0
patience=25
num_epochs = PARAMS['num_epochs']
initial_epoch=0
src_db = pyleaves.DATABASE_PATH
datasets = {
'PNAS': pnas_dataset.PNASDataset(src_db=src_db),
'Leaves': leaves_dataset.LeavesDataset(src_db=src_db),
'Fossil': fossil_dataset.FossilDataset(src_db=src_db)
}
# data = datasets[PARAMS['dataset_name']]
data_config = stuf(threshold=PARAMS['data_threshold'],
num_classes=PARAMS['num_classes'] ,
data_splits_meta={
'train':PARAMS['train_size'],
'val':PARAMS['val_size'],
'test':PARAMS['test_size']
}
)
preprocess_input = get_preprocessing_func(PARAMS['model_name'])
preprocess_input(tf.zeros([4, 224, 224, 3]))
load_example = partial(_load_uint8_example, num_classes=data_config.num_classes)
# load_example = partial(_load_example, num_classes=data_config.num_classes)
if PARAMS['num_channels']==3:
color_aug = {'rgb2gray_3channel':1.0}
elif PARAMS['num_channels']==1:
color_aug = {'rgb2gray_1channel':1.0}
resize_w_pad=None
random_jitter=None
if not PARAMS['random_jitter']['resize']:
resize_w_pad = PARAMS['image_size']
else:
random_jitter=PARAMS['random_jitter']
TRAIN_image_augmentor = ImageAugmentor(name='train',
augmentations={**PARAMS["augmentations"],
**color_aug},#'rotate':1.0,'flip':1.0,**color_aug},
resize_w_pad=resize_w_pad,
random_crop=None,
random_jitter=random_jitter,
log_dir=log_dir,
seed=None)
VAL_image_augmentor = ImageAugmentor(name='val',
augmentations={**color_aug},
resize_w_pad=PARAMS['image_size'],
random_crop=None,
random_jitter=None,
log_dir=log_dir,
seed=None)
TEST_image_augmentor = ImageAugmentor(name='test',
augmentations={**color_aug},
resize_w_pad=PARAMS['image_size'],
random_crop=None,
random_jitter=None,
log_dir=log_dir,
seed=None)
def neptune_log_augmented_images(split_data, num_demo_samples=40, PARAMS=PARAMS):
num_demo_samples = 40
cm_data_x = {'train':[],'val':[]}
cm_data_y = {'train':[],'val':[]}
cm_data_x['train'], cm_data_y['train'] = next(iter(get_data_loader(data=split_data['train'], data_subset_mode='train', batch_size=num_demo_samples, infinite=True, augment=False,seed=2836)))
cm_data_x['val'], cm_data_y['val'] = next(iter(get_data_loader(data=split_data['val'], data_subset_mode='val', batch_size=num_demo_samples, infinite=True, augment=False, seed=2836)))
for (k_x,v_x), (k_y, v_y) in zip(cm_data_x.items(), cm_data_y.items()):
x = tf.data.Dataset.from_tensor_slices(v_x)
y = tf.data.Dataset.from_tensor_slices(v_y)
xy_data = tf.data.Dataset.zip((x, y))
v = xy_data.map(VAL_image_augmentor.resize, num_parallel_calls=AUTOTUNE)
v_aug = TRAIN_image_augmentor.apply_augmentations(xy_data)
v_x, v_y = [i.numpy() for i in next(iter(v.batch(10*num_demo_samples)))]
v_x_aug, v_y_aug = [i.numpy() for i in next(iter(v_aug.batch(10*num_demo_samples)))]
k = k_x
for i in range(num_demo_samples):
print(f'Neptune: logging {k}_{i}')
print(f'{v_x[i].shape}, {v_x_aug[i].shape}')
idx = np.random.randint(0,len(v_x))
if True: #'train' in k:
TRAIN_image_augmentor.logger.add_log(v_x[idx],counter=i, name=k)
TRAIN_image_augmentor.logger.add_log(v_x_aug[idx],counter=i, name=k+'_aug')
def get_data_loader(data : tuple, data_subset_mode='train', batch_size=32, num_classes=None, infinite=True, augment=True, seed=2836):
num_samples = len(data[0])
x = tf.data.Dataset.from_tensor_slices(data[0])
labels = tf.data.Dataset.from_tensor_slices(data[1])
data = tf.data.Dataset.zip((x, labels))
data = data.cache()
if data_subset_mode == 'train':
data = data.shuffle(buffer_size=num_samples)
# data = data.map(lambda x,y: (tf.image.convert_image_dtype(load_img(x)*255.0,dtype=tf.uint8),y), num_parallel_calls=-1)
# data = data.map(load_example, num_parallel_calls=AUTOTUNE)
data = data.map(load_example, num_parallel_calls=AUTOTUNE)
data = data.map(lambda x,y: (preprocess_input(x), y), num_parallel_calls=AUTOTUNE)
if infinite:
data = data.repeat()
if data_subset_mode == 'train':
data = data.shuffle(buffer_size=200, seed=seed)
augmentor = TRAIN_image_augmentor
elif data_subset_mode == 'val':
augmentor = VAL_image_augmentor
elif data_subset_mode == 'test':
augmentor = TEST_image_augmentor
if augment:
data = augmentor.apply_augmentations(data)
data = data.batch(batch_size, drop_remainder=True)
return data.prefetch(AUTOTUNE)
def get_tfds_data_loader(data : tf.data.Dataset, data_subset_mode='train', batch_size=32, num_samples=100, num_classes=19, infinite=True, augment=True, seed=2836):
def encode_example(x, y):
x = tf.image.convert_image_dtype(x, tf.float32) * 255.0
y = _encode_label(y, num_classes=num_classes)
return x, y
test_d = next(iter(data))
print(test_d[0].numpy().min())
print(test_d[0].numpy().max())
data = data.shuffle(buffer_size=num_samples) \
.cache() \
.map(encode_example, num_parallel_calls=AUTOTUNE)
test_d = next(iter(data))
print(test_d[0].numpy().min())
print(test_d[0].numpy().max())
data = data.map(preprocess_input, num_parallel_calls=AUTOTUNE)
test_d = next(iter(data))
print(test_d[0].numpy().min())
print(test_d[0].numpy().max())
if data_subset_mode == 'train':
data = data.shuffle(buffer_size=100, seed=seed)
augmentor = TRAIN_image_augmentor
elif data_subset_mode == 'val':
augmentor = VAL_image_augmentor
elif data_subset_mode == 'test':
augmentor = TEST_image_augmentor
if augment:
data = augmentor.apply_augmentations(data)
test_d = next(iter(data))
print(test_d[0].numpy().min())
print(test_d[0].numpy().max())
data = data.batch(batch_size, drop_remainder=True)
if infinite:
data = data.repeat()
return data.prefetch(AUTOTUNE)
# y_true = [[0, 1, 0], [0, 0, 1]]
# y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
def accuracy(y_true, y_pred):
y_pred = tf.argmax(y_pred, axis=-1)
y_true = tf.argmax(y_true, axis=-1)
return tf.reduce_mean(tf.cast(tf.equal(y_true, y_pred), tf.float32))
def true_pos(y_true, y_pred):
# y_true = K.ones_like(y_true)
return K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
def false_pos(y_true, y_pred):
# y_true = K.ones_like(y_true)
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
all_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
return all_positives - true_positives
def true_neg(y_true, y_pred):
# y_true = K.ones_like(y_true)
return K.sum(1-K.round(K.clip(y_true * y_pred, 0, 1)))
def recall(y_true, y_pred):
# y_true = K.ones_like(y_true)
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
all_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (all_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
y_true = K.ones_like(y_true)
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
# tf.print(y_true, y_pred)
return precision
def f1_score(y_true, y_pred):
m_precision = precision(y_true, y_pred)
m_recall = recall(y_true, y_pred)
# pdb.set_trace()
return 2*((m_precision*m_recall)/(m_precision+m_recall+K.epsilon()))
# def false_neg(y_true, y_pred):
# y_true = K.ones_like(~y_true)
# true_neg = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
# all_negative = K.sum(K.round(K.clip(y_true, 0, 1)))
# return all_negatives - true_
# return K.mean(K.argmax(y_true,axis=1)*K.argmax(y_pred,axis=1))
# 'accuracy',
# metrics.TrueNegatives(name='tn'),
# metrics.FalseNegatives(name='fn'),
METRICS = [
f1_score,
metrics.TruePositives(name='tp'),
metrics.FalsePositives(name='fp'),
metrics.CategoricalAccuracy(name='accuracy'),
metrics.TopKCategoricalAccuracy(name='top_3_categorical_accuracy', k=3),
metrics.TopKCategoricalAccuracy(name='top_5_categorical_accuracy', k=5)
]
PARAMS['sys.argv'] = ' '.join(sys.argv)
with neptune.create_experiment(name=experiment_name, params=PARAMS, upload_source_files=[__file__]):
print('Logging experiment tags:')
for tag in args.tags:
print(tag)
neptune.append_tag(tag)
neptune.append_tag(PARAMS['dataset_name'])
neptune.append_tag(PARAMS['model_name'])
neptune.log_artifact(args.config_path)
cm_data_x = {'train':[],'val':[]}
cm_data_y = {'train':[],'val':[]}
if PARAMS['dataset_name'] in tfds.list_builders():
num_demo_samples=40
tfds_builder = tfds.builder(PARAMS['dataset_name'])
tfds_builder.download_and_prepare()
num_samples = tfds_builder.info.splits['train'].num_examples
num_samples_dict = {'train':int(num_samples*PARAMS['train_size']),
'val':int(num_samples*PARAMS['val_size']),
'test':int(num_samples*PARAMS['test_size'])}
classes = tfds_builder.info.features['label'].names
num_classes = len(classes)
train_slice = [0,int(PARAMS['train_size']*100)]
val_slice = [int(PARAMS['train_size']*100), int((PARAMS['train_size']+PARAMS['val_size'])*100)]
test_slice = [100 - int(PARAMS['test_size']*100), 100]
tfds_train_data = tfds.load(PARAMS['dataset_name'], split=f"train[{train_slice[0]}%:{train_slice[1]}%]", shuffle_files=True, as_supervised=True)
tfds_validation_data = tfds.load(PARAMS['dataset_name'], split=f"train[{val_slice[0]}%:{val_slice[1]}%]", shuffle_files=True, as_supervised=True)
tfds_test_data = tfds.load(PARAMS['dataset_name'], split=f"train[{test_slice[0]}%:{test_slice[1]}%]", shuffle_files=True, as_supervised=True)
# PARAMS['batch_size']=1
train_data = get_tfds_data_loader(data = tfds_train_data, data_subset_mode='train', batch_size=PARAMS['batch_size'], num_samples=num_samples_dict['train'], num_classes=num_classes, infinite=True, augment=True, seed=2836)
validation_data = get_tfds_data_loader(data = tfds_validation_data, data_subset_mode='val', batch_size=PARAMS['batch_size'], num_samples=num_samples_dict['val'], num_classes=num_classes, infinite=True, augment=True, seed=2837)
test_data = get_tfds_data_loader(data = tfds_test_data, data_subset_mode='test', batch_size=PARAMS['batch_size'], num_samples=num_samples_dict['test'], num_classes=num_classes, infinite=True, augment=True, seed=2838)
# tfds_train_data = tfds.load(PARAMS['dataset_name'], split=f"train[{train_slice[0]}%:{train_slice[1]}%]", shuffle_files=True, as_supervised=True)
# tfds_validation_data = tfds.load(PARAMS['dataset_name'], split=f"train[{val_slice[0]}%:{val_slice[1]}%]", shuffle_files=True, as_supervised=True)
# tfds_test_data = tfds.load(PARAMS['dataset_name'], split=f"train[{test_slice[0]}%:{test_slice[1]}%]", shuffle_files=True, as_supervised=True)
split_data = {'train':get_tfds_data_loader(data = tfds_train_data, data_subset_mode='train', batch_size=num_demo_samples, num_samples=num_samples_dict['train'], num_classes=num_classes, infinite=True, augment=True, seed=2836),
'val':get_tfds_data_loader(data = tfds_validation_data, data_subset_mode='val', batch_size=num_demo_samples, num_samples=num_samples_dict['val'], num_classes=num_classes, infinite=True, augment=True, seed=2837),
'test':get_tfds_data_loader(data = tfds_test_data, data_subset_mode='test', batch_size=num_demo_samples, num_samples=num_samples_dict['test'], num_classes=num_classes, infinite=True, augment=True, seed=2838)
}
steps_per_epoch=num_samples_dict['train']//PARAMS['batch_size']
validation_steps=num_samples_dict['val']//PARAMS['batch_size']
cm_data_x['train'], cm_data_y['train'] = next(iter(split_data['train']))
cm_data_x['val'], cm_data_y['val'] = next(iter(split_data['val']))
else:
data = datasets[PARAMS['dataset_name']]
neptune.set_property('num_classes',data.num_classes)
neptune.set_property('class_distribution',data.metadata.class_distribution)
encoder = base_dataset.LabelEncoder(data.data.family)
split_data = base_dataset.preprocess_data(data, encoder, data_config)
# import pdb;pdb.set_trace()
for subset, subset_data in split_data.items():
split_data[subset] = [list(i) for i in unzip(subset_data)]
PARAMS['batch_size'] = 32
steps_per_epoch=len(split_data['train'][0])//PARAMS['batch_size']#//10
validation_steps=len(split_data['val'][0])//PARAMS['batch_size']#//10
split_datasets = {
k:base_dataset.BaseDataset.from_dataframe(
pd.DataFrame({
'path':v[0],
'family':v[1]
})) \
for k,v in split_data.items()
}
for k,v in split_datasets.items():
print(k, v.num_classes)
classes = split_datasets['train'].classes
train_data=get_data_loader(data=split_data['train'], data_subset_mode='train', batch_size=PARAMS['batch_size'], infinite=True, augment=True, seed=2836)
validation_data=get_data_loader(data=split_data['val'], data_subset_mode='val', batch_size=PARAMS['batch_size'], infinite=True, augment=True, seed=2837)
if 'test' in split_data.keys():
test_data=get_data_loader(data=split_data['test'], data_subset_mode='test', batch_size=PARAMS['batch_size'], infinite=True, augment=True, seed=2838)
num_demo_samples=150
# neptune_log_augmented_images(split_data, num_demo_samples=num_demo_samples, PARAMS=PARAMS)
cm_data_x['train'], cm_data_y['train'] = next(iter(get_data_loader(data=split_data['train'], data_subset_mode='train', batch_size=num_demo_samples, infinite=True, augment=True, seed=2836)))
cm_data_x['val'], cm_data_y['val'] = next(iter(get_data_loader(data=split_data['val'], data_subset_mode='val', batch_size=num_demo_samples, infinite=True, augment=True, seed=2836)))
########################################################################################
train_image_logger_cb = ImageLoggerCallback(data=train_data, freq=20, max_images=-1, name='train', encoder=encoder)
val_image_logger_cb = ImageLoggerCallback(data=validation_data, freq=20, max_images=-1, name='val', encoder=encoder)
########################################################################################
cm_callback = ConfusionMatrixCallback(log_dir, cm_data_x, cm_data_y, classes=classes, seed=PARAMS['seed'], include_train=True)
checkpoint = ModelCheckpoint(weights_best, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='min',restore_best_weights=restore_best_weights)
tfboard = TensorBoard(log_dir=log_dir, histogram_freq=histogram_freq, write_images=True)
early = EarlyStopping(monitor='val_loss', patience=patience, verbose=1)
callbacks = [checkpoint,tfboard,early, cm_callback, neptune_logger, train_image_logger_cb, val_image_logger_cb]
##########################
if PARAMS['optimizer'] == 'Adam':
optimizer = tf.keras.optimizers.Adam(
learning_rate=PARAMS['lr']
)
elif PARAMS['optimizer'] == 'Nadam':
optimizer = tf.keras.optimizers.Nadam(
learning_rate=PARAMS['lr']
)
elif PARAMS['optimizer'] == 'SGD':
optimizer = tf.keras.optimizers.SGD(
learning_rate=PARAMS['lr']
)
##########################
if PARAMS['loss']=='focal_loss':
loss = focal_loss(gamma=2.0, alpha=4.0)
elif PARAMS['loss']=='categorical_crossentropy':
loss = 'categorical_crossentropy'
##########################
model_params = stuf(name=PARAMS['model_name'],
model_dir=os.path.join(experiment_dir, experiment_name, 'models'),
num_classes=PARAMS['num_classes'],
frozen_layers = PARAMS['frozen_layers'],
input_shape = (*PARAMS['image_size'],PARAMS['num_channels']),
base_learning_rate = PARAMS['lr'],
regularization = PARAMS['regularization'])
####
if PARAMS['model_name']=='shallow':
model = build_shallow(input_shape=model_params.input_shape,
num_classes=PARAMS['num_classes'],
optimizer=optimizer,
loss=loss,
METRICS=METRICS)
else:
model = build_model(model_params,
optimizer,
loss,
METRICS)
print(f"TRAINING {PARAMS['model_name']}")
model.summary(print_fn=lambda x: neptune.log_text('model_summary', x))
history = model.fit(train_data,
epochs=num_epochs,
callbacks=callbacks,
validation_data=validation_data,
shuffle=True,
initial_epoch=initial_epoch,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps)
if 'test' in split_data:
results = model.evaluate(test_data,
steps=len(split_data['test'][0]))
else:
results = model.evaluate(validation_data,
steps=validation_steps)
def main():
# ================= Arguments ================ #
parser = argparse.ArgumentParser(description='PyTorch Knowledge Distillation')
parser.add_argument('--gpu', type=str, default="4", help='gpu id')
parser.add_argument('--config', type=str, default="config", help='.json')
args = parser.parse_args()
# ================= Device Setup ================ #
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ================= Config Load ================ #
with open('config/' + args.config) as config_file:
config = json.load(config_file)
# ================= Neptune Setup ================ #
if config['neptune']:
neptune.init('seongjulee/DCENet', api_token=config["neptune_token"]) # username/project-name, api_token=token from neptune
neptune.create_experiment(name='EXP', params=config) # name=project name (anything is ok), params=parameter list (json format)
neptune.append_tag(args.config) # neptune tag (str or string list)
# ================= Model Setup ================ #
model = nn.DataParallel(DCENet(config)).to(device) if len(args.gpu.split(',')) > 1 else DCENet(config).to(device)
# ================= Loss Function ================ #
criterion = DCENetLoss(config)
# ================= Optimizer Setup ================ #
optimizer = optim.Adam(model.parameters(), lr=config['lr'], betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-6, amsgrad=False)
# ================= Data Loader ================ #
datalist = DataInfo()
train_datalist = datalist.train_merged
print('Train data list', train_datalist)
test_datalist = datalist.train_biwi
print('Test data list', test_datalist)
np.random.seed(10)
offsets, traj_data, occupancy = load_data(config, train_datalist, datatype="train")
trainval_split = np.random.rand(len(offsets)) < config['split']
train_x = offsets[trainval_split, :config['obs_seq'] - 1, 4:6]
train_occu = occupancy[trainval_split, :config['obs_seq'] - 1, ..., :config['enviro_pdim'][-1]]
train_y = offsets[trainval_split, config['obs_seq'] - 1:, 4:6]
train_y_occu = occupancy[trainval_split, config['obs_seq'] - 1:, ..., :config['enviro_pdim'][-1]]
val_x = offsets[~trainval_split, :config['obs_seq'] - 1, 4:6]
val_occu = occupancy[~trainval_split, :config['obs_seq'] - 1, ..., :config['enviro_pdim'][-1]]
val_y = offsets[~trainval_split, config['obs_seq'] - 1:, 4:6]
val_y_occu = occupancy[~trainval_split, config['obs_seq'] - 1:, ..., :config['enviro_pdim'][-1]]
print("%.0f trajectories for training\n %.0f trajectories for valiadation" %(train_x.shape[0], val_x.shape[0]))
test_offsets, test_trajs, test_occupancy = load_data(config, test_datalist, datatype="test")
test_x = test_offsets[:, :config['obs_seq'] - 1, 4:6]
test_occu = test_occupancy[:, :config['obs_seq'] - 1, ..., :config['enviro_pdim'][-1]]
last_obs_test = test_offsets[:, config['obs_seq'] - 2, 2:4]
y_truth = test_offsets[:, config['obs_seq'] - 1:, :4]
xy_truth = test_offsets[:, :, :4]
print('test_trajs', test_trajs.shape)
print("%.0f trajectories for testing" % (test_x.shape[0]))
train_dataset = TrajDataset(x=train_x, x_occu=train_occu, y=train_y, y_occu=train_y_occu, mode='train')
train_loader = DataLoader(dataset=train_dataset, batch_size=config["batch_size"], shuffle=True, num_workers=4)
val_dataset = TrajDataset(x=val_x, x_occu=val_occu, y=val_y, y_occu=val_y_occu, mode='val')
val_loader = DataLoader(dataset=val_dataset, batch_size=config["batch_size"], shuffle=False, num_workers=4)
# test_dataset = TrajDataset(x=test_x, x_occu=test_occu, y=y_truth, y_occu=None, mode='test')
# test_loader = DataLoader(dataset=test_dataset, batch_size=config["batch_size"], shuffle=False, num_workers=4)
# ================= Training Loop ================ #
early_stopping = EarlyStopping(patience=config['patience'], verbose=True, filename=args.config.split('/')[-1].replace('.json', '.pth'))
for epoch in range(config['max_epochs']):
train_one_epoch(config, epoch, device, model, optimizer, criterion, train_loader)
val_loss = evaluate(config, device, model, optimizer, criterion, val_loader)
early_stopping(val_loss, model)
if early_stopping.early_stop:
print("Early stopping")
break
# ================= Test ================ #
model.load_state_dict(torch.load(os.path.join('checkpoints', args.config.split('/')[-1].replace('.json', '.pth'))))
model.eval()
with torch.no_grad():
test_x, test_occu = input2tensor(test_x, test_occu, device)
x_latent = model.encoder_x(test_x, test_occu)
predictions = []
for i, x_ in enumerate(x_latent):
last_pos = last_obs_test[i]
x_ = x_.view(1, -1)
for i in range(config['num_pred']):
y_p = model.decoder(x_, train=False)
y_p_ = np.concatenate(([last_pos], np.squeeze(y_p.cpu().numpy())), axis=0)
y_p_sum = np.cumsum(y_p_, axis=0)
predictions.append(y_p_sum[1:, :])
predictions = np.reshape(predictions, [-1, config['num_pred'], config['pred_seq'], 2])
print('Predicting done!')
print(predictions.shape)
plot_pred(xy_truth, predictions)
# Get the errors for ADE, DEF, Hausdorff distance, speed deviation, heading error
print("\nEvaluation results @top%.0f" % config['num_pred'])
errors = get_errors(y_truth, predictions)
check_collision(y_truth)
## Get the first time prediction by g
ranked_prediction = []
for prediction in predictions:
ranks = gauss_rank(prediction)
ranked_prediction.append(prediction[np.argmax(ranks)])
ranked_prediction = np.reshape(ranked_prediction, [-1, 1, config['pred_seq'], 2])
print("\nEvaluation results for most-likely predictions")
ranked_errors = get_errors(y_truth, ranked_prediction)
import neptune
import numpy as np
# Select project
neptune.init('neptune-workshops/AII-Optimali')
# Define parameters
PARAMS = {'decay_factor': 0.5, 'n_iterations': 117}
# Create experiment
neptune.create_experiment(name='minimal-extended', params=PARAMS)
# Log some metrics
for i in range(1, PARAMS['n_iterations']):
neptune.log_metric('iteration', i)
neptune.log_metric('loss', PARAMS['decay_factor'] / i**0.5)
neptune.log_text('text_info', 'some value {}'.format(0.95 * i**2))
# Add tag to the experiment
neptune.append_tag('quick_start')
# Log some images
for j in range(5):
array = np.random.rand(10, 10, 3) * 255
array = np.repeat(array, 30, 0)
array = np.repeat(array, 30, 1)
neptune.log_image('mosaics', array)
def Eval_phase(params,which_files='test',model=None,test_dataloader=None,device=None):
if(params['is_model']==True):
print("model previously passed")
model.eval()
else:
return 1
# ### Have to modify in the final run
# model=select_model(params['what_bert'],params['path_files'],params['weights'])
# model.cuda()
# model.eval()
print("Running eval on ",which_files,"...")
t0 = time.time()
# Put the model in evaluation mode--the dropout layers behave differently
# during evaluation.
# Tracking variables
true_labels=[]
pred_labels=[]
logits_all=[]
# Evaluate data for one epoch
for step, batch in tqdm(enumerate(test_dataloader)):
# Progress update every 40 batches.
if step % 40 == 0 and not step == 0:
# Calculate elapsed time in minutes.
elapsed = format_time(time.time() - t0)
# `batch` contains three pytorch tensors:
# [0]: input ids
# [1]: attention vals
# [2]: attention mask
# [3]: labels
b_input_ids = batch[0].to(device)
b_att_val = batch[1].to(device)
b_input_mask = batch[2].to(device)
b_labels = batch[3].to(device)
# (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
model.zero_grad()
outputs = model(b_input_ids,
attention_vals=b_att_val,
attention_mask=b_input_mask,
labels=None,device=device)
logits = outputs[0]
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# Calculate the accuracy for this batch of test sentences.
# Accumulate the total accuracy.
pred_labels+=list(np.argmax(logits, axis=1).flatten())
true_labels+=list(label_ids.flatten())
logits_all+=list(logits)
logits_all_final=[]
for logits in logits_all:
logits_all_final.append(softmax(logits))
testf1=f1_score(true_labels, pred_labels, average='macro')
testacc=accuracy_score(true_labels,pred_labels)
if(params['num_classes']==3):
testrocauc=roc_auc_score(true_labels, logits_all_final,multi_class='ovo',average='macro')
else:
#testrocauc=roc_auc_score(true_labels, logits_all_final,multi_class='ovo',average='macro')
testrocauc=0
testprecision=precision_score(true_labels, pred_labels, average='macro')
testrecall=recall_score(true_labels, pred_labels, average='macro')
if(params['logging']!='neptune' or params['is_model'] == True):
# Report the final accuracy for this validation run.
print(" Accuracy: {0:.2f}".format(testacc))
print(" Fscore: {0:.2f}".format(testf1))
print(" Precision: {0:.2f}".format(testprecision))
print(" Recall: {0:.2f}".format(testrecall))
print(" Roc Auc: {0:.2f}".format(testrocauc))
print(" Test took: {:}".format(format_time(time.time() - t0)))
#print(ConfusionMatrix(true_labels,pred_labels))
else:
bert_model = params['path_files']
language = params['language']
name_one=bert_model+"_"+language
neptune.create_experiment(name_one,params=params,send_hardware_metrics=False,run_monitoring_thread=False)
neptune.append_tag(bert_model)
neptune.append_tag(language)
neptune.append_tag('test')
neptune.log_metric('test_f1score',testf1)
neptune.log_metric('test_accuracy',testacc)
neptune.log_metric('test_precision',testprecision)
neptune.log_metric('test_recall',testrecall)
neptune.log_metric('test_rocauc',testrocauc)
neptune.stop()
return testf1,testacc,testprecision,testrecall,testrocauc,logits_all_final
parser.add_argument('--weight_decay', type=float, help='')
parser.add_argument('--max_epoch', type=int, help='')
parser.add_argument('--valid_every', type=int, help='')
parser.add_argument('--out_dir', type=str, help='')
parser.add_argument('--out_file', type=str, help='')
parser.add_argument('--patience', type=int, help='')
parser.add_argument('--is_train', type=int, help='')
parser.add_argument('--dim_input', type=int, help='')
parser.add_argument('--dim_out', type=int, help='')
parser.add_argument('--dim_lstm_hidden', type=int, help='')
parser.add_argument('--dim_fc_hidden', type=int, help='')
parser.add_argument('--rnn_len', type=int, help='')
parser.add_argument('--name', type=str, help='')
parser.add_argument('--tag', type=str, help='')
parser.add_argument('--n_cmt', type=int, help='')
args = parser.parse_args()
params = vars(args)
neptune.init('cjlee/AnomalyDetection-Supervised-RNN')
experiment = neptune.create_experiment(name=args.name, params=params)
neptune.append_tag(args.tag)
args.out_dir = './result'
args.out_file = experiment.id + '.pth'
# temporary code for testing
train_main(args, neptune)
# Directory to save the pretrained model
parser.add_argument("--save_dir",
default="./resource/checkpoint/zinc_daga/")
args = parser.parse_args()
# Initialize random seed and prepare CUDA device
device = torch.device(0)
random.seed(0)
# Initialize neptune
neptune.init(
project_qualified_name="sungsoo.ahn/deep-molecular-optimization")
neptune.create_experiment(name="pretrain", params=vars(args))
neptune.append_tag(args.dataset)
# Load character dict and dataset
char_dict = SmilesCharDictionary(dataset=args.dataset,
max_smi_len=args.max_smiles_length)
dataset = load_dataset(char_dict=char_dict, smi_path=args.dataset_path)
# Prepare neural apprentice. We set max_sampling_batch_size=0 since we do not use sampling.
input_size = max(char_dict.char_idx.values()) + 1
generator = SmilesGenerator(
input_size=input_size,
hidden_size=args.hidden_size,
output_size=input_size,
n_layers=args.n_layers,
lstm_dropout=args.lstm_dropout,
)
def training_pipeline(args):
###############################################################################
# Environment setup
###############################################################################
# Set the random seed manually for reproducibility.
random.seed(args.seed)
torch.manual_seed(args.seed)
# Check if CUDA device is available and set training on CPUs or GPUs
if torch.cuda.is_available():
if not args.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
device = torch.device(args.cuda_device if args.cuda else "cpu")
###############################################################################
# Experiment tracking setup
###############################################################################
neptune.init(project_qualified_name='karexar/GSW-dialect-classifier')
args_dict = vars(args)
neptune.create_experiment(params=args_dict)
if hasattr(args, 'experiment_id'):
neptune.append_tag(args.experiment_id)
neptune.set_property('lm_algo', 'lstm')
for key in args_dict.keys():
neptune.set_property(key, args_dict[key])
###############################################################################
# Load data
###############################################################################
print('Loading data')
data_manager = DataManager(args.data, device, args.batch_size,
args.eval_batch_size)
###############################################################################
# Build the model
###############################################################################
print('Building model')
num_tokens = data_manager.vocab_size
num_labels = data_manager.num_labels
embeddings_matrix = None
if args.use_pretrained_embed:
# Load pre-trained word embeddings model
# and generate the embeddings weight matrix for the entire vocabulary
assert args.embed_algo is not None
print(f'Using {args.embed_algo} pre-trained word embeddings')
if args.embed_algo == 'word2vec':
pretrained_embeddings = Word2VecModel(args.model_path_embed,
args.model_name_embed,
load_from_disk=True)
embeddings_matrix = pretrained_embeddings.get_vocabulary_embeddings(
data_manager.idx2word, args.embed_size)
elif args.embed_algo == 'glove':
pretrained_embeddings = GloveModel(args.model_path_embed,
args.model_name_embed,
load_from_disk=True)
embeddings_matrix = pretrained_embeddings.get_vocabulary_embeddings(
data_manager.idx2word, args.embed_size)
model = LSTM(num_tokens, args.embed_size, args.num_hidden, args.num_layers,
args.dropout, num_labels, embeddings_matrix).to(device)
print('Model architecture')
print(model)
criterion = nn.CrossEntropyLoss()
###############################################################################
# Training code
###############################################################################
print('Initialising model executor')
model_executor = ModelExecutor(model, data_manager, device, criterion)
if args.train_lstm:
# Loop over epochs
learning_rate = args.learning_rate
best_val_accuracy = None
last_val_accuracy = 0
model_optimiser = optim.SGD(model.parameters(), lr=learning_rate)
# At any point you can hit Ctrl + C to break out of training early.
try:
print('Starting the training process')
for epoch in range(1, args.epochs + 1):
epoch_start_time = time.time()
_, _ = model_executor.train(epoch, args.batch_size,
learning_rate, model_optimiser,
args.clip, args.log_interval)
val_loss, val_accuracy = model_executor.evaluate(
data_manager.val_iter, args.eval_batch_size)
# Log result in Neptune ML
neptune.send_metric('valid_loss', epoch, val_loss)
neptune.send_metric('valid_accuracy', epoch, val_accuracy)
neptune.send_metric('learning_rate', epoch, learning_rate)
if epoch % 3 == 0:
learning_rate *= 0.9 # correct the learning rate after some number of epochs
print('-' * 89)
print(
'| End of epoch {:3d} | Time: {:5.2f}s | Valid loss {:6.2f} | '
'Valid accuracy {:8.2f}'.format(
epoch, (time.time() - epoch_start_time), val_loss,
val_accuracy))
print('-' * 89)
# Save the model if the validation accuracy is the best we've seen so far.
if not best_val_accuracy or val_accuracy > best_val_accuracy:
model_executor.model.export_model(args.model_path_lstm)
best_val_accuracy = val_accuracy
if val_accuracy < last_val_accuracy:
# Anneal the learning rate if no improvement has been seen in the validation dataset.
learning_rate /= 2.0
for group in model_optimiser.param_groups:
group['lr'] = learning_rate
last_val_accuracy = val_accuracy
except KeyboardInterrupt:
print('-' * 89)
print('Exiting from training early')
###############################################################################
# Evaluation code
###############################################################################
test_loss = None
test_accuracy = None
if args.eval_lstm:
print('Evaluating on the test set')
# Load the best saved model.
model_executor.load_pre_trained_model(args.model_path_lstm,
device=device)
# Run on test data.
test_loss, test_accuracy = model_executor.evaluate(
data_manager.test_iter, args.eval_batch_size)
# Log result in Neptune ML
neptune.send_metric('test_loss', test_loss)
neptune.send_metric('test_accuracy', test_accuracy)
print('-' * 89)
print('| End of evaluation | Test loss {:6.2f}'.format(test_loss) +
' | Test accuracy {:8.2f}'.format(test_accuracy))
print('-' * 89)
###############################################################################
# Stop the experiment tracking
###############################################################################
neptune.stop()
return test_loss, test_accuracy
parameters['metrics_separately'] = args.metrics_separately
parameters['random_val_neg_sampler'] = args.random_val_neg_sampler
parameters['val_regenerate'] = args.val_regenerate
if args.log:
import neptune
neptune.init(args.neptune_project)
neptune_experiment_name = args.experiment_name
neptune.create_experiment(name=neptune_experiment_name,
params=parameters,
upload_stdout=True,
upload_stderr=True,
send_hardware_metrics=True,
upload_source_files='**/*.py')
neptune.append_tag('pytorch')
if args.gpu:
neptune.append_tag('gpu')
if args.use_proteins:
neptune.append_tag('proteins')
if args.reversed:
neptune.append_tag('reversed')
neptune.append_tag('real data')
neptune.append_tag('trivec')
use_cuda = args.gpu and torch.cuda.is_available()
device = torch.device("cuda" if args.gpu else "cpu")
print(f'Use device: {device}')
kg = KnowledgeGraph(data_path=DATA_CONST['work_dir'],
use_proteins=args.use_proteins,
def train_model(params, best_val_fscore):
# In case of english languages, translation is the origin data itself.
if (params['language'] == 'English'):
params['csv_file'] = '*_full.csv'
train_path = params['files'] + '/train/' + params['csv_file']
val_path = params['files'] + '/val/' + params['csv_file']
# Load the training and validation datasets
train_files = glob.glob(train_path)
val_files = glob.glob(val_path)
#Load the bert tokenizer
print('Loading BERT tokenizer...')
tokenizer = BertTokenizer.from_pretrained(params['path_files'],
do_lower_case=False)
df_train = data_collector(train_files, params, True)
df_val = data_collector(val_files, params, False)
# Get the comment texts and corresponding labels
if (params['csv_file'] == '*_full.csv'):
sentences_train = df_train.text.values
sentences_val = df_val.text.values
elif (params['csv_file'] == '*_translated.csv'):
sentences_train = df_train.translated.values
sentences_val = df_val.translated.values
labels_train = df_train.label.values
labels_val = df_val.label.values
label_counts = df_train['label'].value_counts()
print(label_counts)
label_weights = [(len(df_train)) / label_counts[0],
len(df_train) / label_counts[1]]
print(label_weights)
# Select the required bert model. Refer below for explanation of the parameter values.
model = select_model(params['what_bert'], params['path_files'],
params['weights'])
# Tell pytorch to run this model on the GPU.
model.cuda()
# Do the required encoding using the bert tokenizer
input_train_ids, att_masks_train = combine_features(
sentences_train, tokenizer, params['max_length'])
input_val_ids, att_masks_val = combine_features(sentences_val, tokenizer,
params['max_length'])
# Create dataloaders for both the train and validation datasets.
train_dataloader = return_dataloader(input_train_ids,
labels_train,
att_masks_train,
batch_size=params['batch_size'],
is_train=params['is_train'])
validation_dataloader = return_dataloader(input_val_ids,
labels_val,
att_masks_val,
batch_size=params['batch_size'],
is_train=False)
# Initialize AdamW optimizer.
optimizer = AdamW(
model.parameters(),
lr=params[
'learning_rate'], # args.learning_rate - default is 5e-5, our notebook had 2e-5
eps=params['epsilon'] # args.adam_epsilon - default is 1e-8.
)
# Number of training epochs (authors recommend between 2 and 4)
# Total number of training steps is number of batches * number of epochs.
total_steps = len(train_dataloader) * params['epochs']
# Create the learning rate scheduler.
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=int(total_steps / 10), # Default value in run_glue.py
num_training_steps=total_steps)
# Set the seed value all over the place to make this reproducible.
fix_the_random(seed_val=params['random_seed'])
# Store the average loss after each epoch so we can plot them.
loss_values = []
# Create a new experiment in neptune for this run.
bert_model = params['path_files']
language = params['language']
name_one = bert_model + "_" + language
if (params['logging'] == 'neptune'):
neptune.create_experiment(name_one,
params=params,
send_hardware_metrics=False,
run_monitoring_thread=False)
neptune.append_tag(bert_model)
neptune.append_tag(language)
# The best val fscore obtained till now, for the purpose of hyper parameter finetuning.
best_val_fscore = best_val_fscore
# For each epoch...
for epoch_i in range(0, params['epochs']):
print("")
print('======== Epoch {:} / {:} ========'.format(
epoch_i + 1, params['epochs']))
print('Training...')
# Measure how long the training epoch takes.
t0 = time.time()
# Reset the total loss for this epoch.
total_loss = 0
model.train()
# For each batch of training data...
for step, batch in tqdm(enumerate(train_dataloader)):
# Progress update every 40 batches.
if step % 40 == 0 and not step == 0:
# Calculate elapsed time in minutes.
elapsed = format_time(time.time() - t0)
# `batch` contains three pytorch tensors:
# [0]: input ids
# [1]: attention masks
# [2]: labels
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
# (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
model.zero_grad()
# Get the model outputs for this batch.
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
# The call to `model` always returns a tuple, so we need to pull the
# loss value out of the tuple.
loss = outputs[0]
if (params['logging'] == 'neptune'):
neptune.log_metric('batch_loss', loss)
# Accumulate the training loss over all of the batches so that we can
# calculate the average loss at the end. `loss` is a Tensor containing a
# single value; the `.item()` function just returns the Python value
# from the tensor.
total_loss += loss.item()
# Perform a backward pass to calculate the gradients.
loss.backward()
# Clip the norm of the gradients to 1.0.
# This is to help prevent the "exploding gradients" problem.
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# Update parameters and take a step using the computed gradient.
# The optimizer dictates the "update rule"--how the parameters are
# modified based on their gradients, the learning rate, etc.
optimizer.step()
# Update the learning rate.
scheduler.step()
# Calculate the average loss over the training data.
avg_train_loss = total_loss / len(train_dataloader)
if (params['logging'] == 'neptune'):
neptune.log_metric('avg_train_loss', avg_train_loss)
# Store the loss value for plotting the learning curve.
loss_values.append(avg_train_loss)
# Compute the metrics on the validation and test sets.
val_fscore, val_accuracy = Eval_phase(params, 'val', model)
test_fscore, test_accuracy = Eval_phase(params, 'test', model)
#Report the final accuracy and fscore for this validation run.
if (params['logging'] == 'neptune'):
neptune.log_metric('val_fscore', val_fscore)
neptune.log_metric('val_acc', val_accuracy)
neptune.log_metric('test_fscore', test_fscore)
neptune.log_metric('test_accuracy', test_accuracy)
# Save the model only if the validation fscore improves. After all epochs, the best model is the final saved one.
if (val_fscore > best_val_fscore):
print(val_fscore, best_val_fscore)
best_val_fscore = val_fscore
save_model(model, tokenizer, params)
if (params['logging'] == 'neptune'):
neptune.stop()
del model
torch.cuda.empty_cache()
return fscore, best_val_fscore
state = {
'net': net.state_dict(),
'acc': acc,
'epoch': epoch,
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
torch.save(state, './checkpoint/ckpt'+run_start_time+'.pth')
best_acc = acc
your_file.close()
if __name__ == '__main__':
# writer = SummaryWriter(log_dir="/home/dltdc/data/projects_logs/water_logs/", filename_suffix=run_start_time)
trainloader, testloader = loaddata(load_water_data=True)
net, criterion, optimizer = loadmodel(nb_class=1, img_HW=256, pretrain_model="resnet50")
# trainloader, testloader = loaddata()
# net, criterion, optimizer = loadmodel(nb_class=10, img_HW=8, pretrain_model='resnet18')
with neptune.create_experiment(name='new-model'):
neptune.append_tag('first')
for epoch in range(start_epoch, start_epoch+args.nb_epoch):
train(epoch, net, criterion, optimizer, trainloader)
test(epoch, net, criterion, optimizer, testloader)
# writer.close()
def main():
if FLAGS.exp == 'celebA':
opts = configs.config_celebA
elif FLAGS.exp == 'celebA_small':
opts = configs.config_celebA_small
elif FLAGS.exp == 'mnist':
opts = configs.config_mnist
elif FLAGS.exp == 'mnist_ord':
opts = configs.config_mnist_ord
elif FLAGS.exp == 'mnist_small':
opts = configs.config_mnist_small
elif FLAGS.exp == 'dsprites':
opts = configs.config_dsprites
elif FLAGS.exp == 'grassli':
opts = configs.config_grassli
elif FLAGS.exp == 'grassli_small':
opts = configs.config_grassli_small
elif FLAGS.exp == 'syn_constant_uniform':
opts = configs.config_syn_constant_uniform
elif FLAGS.exp == 'syn_2_constant_uniform':
opts = configs.config_syn_2_constant_uniform
elif FLAGS.exp == 'checkers':
opts = configs.config_checkers
elif FLAGS.exp == 'noise':
opts = configs.config_noise
elif FLAGS.exp == 'noise_unif':
opts = configs.config_noise_unif
else:
assert False, 'Unknown experiment configuration'
opts['exp'] = FLAGS.exp
opts['seed'] = FLAGS.seed
opts['mode'] = FLAGS.mode
if opts['mode'] == 'test':
assert FLAGS.checkpoint is not None, 'Checkpoint must be provided'
opts['checkpoint'] = FLAGS.checkpoint
if FLAGS.batch_size is not None:
opts['batch_size'] = FLAGS.batch_size
if FLAGS.recalculate_size is not None:
opts['recalculate_size'] = FLAGS.recalculate_size
assert opts['recalculate_size'] >= opts[
'batch_size'], "recalculate_size should be at least as large as batch_size"
else:
opts['recalculate_size'] = opts['batch_size']
if FLAGS.zdim is not None:
opts['zdim'] = FLAGS.zdim
if FLAGS.pz is not None:
opts['pz'] = FLAGS.pz
if FLAGS.lr is not None:
opts['lr'] = FLAGS.lr
if FLAGS.lr_schedule is not None:
opts['lr_schedule'] = FLAGS.lr_schedule
if FLAGS.w_aef is not None:
opts['w_aef'] = FLAGS.w_aef
if FLAGS.z_test is not None:
opts['z_test'] = FLAGS.z_test
if FLAGS.lambda_schedule is not None:
opts['lambda_schedule'] = FLAGS.lambda_schedule
if FLAGS.work_dir is not None:
opts['work_dir'] = FLAGS.work_dir
if FLAGS.wae_lambda is not None:
opts['lambda'] = FLAGS.wae_lambda
if FLAGS.enc_noise is not None:
opts['e_noise'] = FLAGS.enc_noise
if FLAGS.z_test_scope is not None:
opts['z_test_scope'] = FLAGS.z_test_scope
if FLAGS.length_lambda is not None:
opts['length_lambda'] = FLAGS.length_lambda
if FLAGS.grad_clip is not None:
opts['grad_clip'] = FLAGS.grad_clip
else:
opts['grad_clip'] = None
if FLAGS.rec_lambda is not None:
opts['rec_lambda'] = FLAGS.rec_lambda
if FLAGS.zxz_lambda is not None:
opts['zxz_lambda'] = FLAGS.zxz_lambda
if FLAGS.train_size is not None:
opts['train_size'] = FLAGS.train_size
if FLAGS.nat_size is not None:
opts['nat_size'] = FLAGS.nat_size
else:
opts['nat_size'] = FLAGS.train_size
opts['nat_resampling'] = FLAGS.nat_resampling
opts['sinkhorn_sparse'] = FLAGS.sinkhorn_sparse
opts['sinkhorn_sparsifier'] = FLAGS.sinkhorn_sparsifier
opts['sparsifier_freq'] = FLAGS.sparsifier_freq
opts['sinkhorn_unbiased'] = FLAGS.sinkhorn_unbiased
opts['feed_by_score_from_epoch'] = FLAGS.feed_by_score_from_epoch
opts['recalculate_size'] = FLAGS.recalculate_size
opts['stay_lambda'] = FLAGS.stay_lambda
opts['mover_ratio'] = FLAGS.mover_ratio
assert opts['mover_ratio'] >= 0 and opts[
'mover_ratio'] <= 1, "mover_ratio must be in [0,1]"
if FLAGS.sinkhorn_iters is not None:
opts['sinkhorn_iters'] = FLAGS.sinkhorn_iters
if FLAGS.sinkhorn_epsilon is not None:
opts['sinkhorn_epsilon'] = FLAGS.sinkhorn_epsilon
if FLAGS.name is not None:
opts['name'] = FLAGS.name
if FLAGS.tags is not None:
opts['tags'] = FLAGS.tags
if FLAGS.epoch_num is not None:
opts['epoch_num'] = FLAGS.epoch_num
if FLAGS.e_pretrain is not None:
opts['e_pretrain'] = FLAGS.e_pretrain
if FLAGS.shuffle is not None:
opts['shuffle'] = FLAGS.shuffle
if opts['verbose']:
pass
#logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(message)s')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
utils.create_dir(opts['work_dir'])
utils.create_dir(os.path.join(opts['work_dir'], 'checkpoints'))
if opts['e_noise'] == 'gaussian' and opts['pz'] != 'normal':
assert False, 'Gaussian encoders compatible only with Gaussian prior'
return
# Dumping all the configs to the text file
with utils.o_gfile((opts['work_dir'], 'params.txt'), 'w') as text:
text.write('Parameters:\n')
for key in opts:
text.write('%s : %s\n' % (key, opts[key]))
# Loading the dataset
data = DataHandler(opts)
assert data.num_points >= opts['batch_size'], 'Training set too small'
if 'train_size' in opts and opts['train_size'] is not None:
train_size = opts['train_size']
else:
train_size = data.num_points
print("Train size:", train_size)
if opts['nat_size'] == -1:
opts['nat_size'] = train_size
use_neptune = "NEPTUNE_API_TOKEN" in os.environ
if opts['mode'] == 'train':
if use_neptune:
neptune.init(project_qualified_name="csadrian/global-sinkhorn")
exp = neptune.create_experiment(
params=opts,
name=opts['name'],
upload_source_files=['wae.py', 'run.py', 'models.py'])
for tag in opts['tags'].split(','):
neptune.append_tag(tag)
# Creating WAE model
wae = WAE(opts, train_size)
data.num_points = train_size
# Training WAE
wae.train(data)
if use_neptune:
exp.stop()
elif opts['mode'] == 'test':
# Do something else
improved_wae.improved_sampling(opts)
elif opts['mode'] == 'generate':
fideval.generate(opts)
elif opts['mode'] == 'draw':
picture_plot.createimgs(opts)
def __init__(self, tags):
neptune.set_project('pixelneo/whoosh')
neptune.create_experiment()
for tag in tags:
neptune.append_tag(tag)
def train_imagenette(PARAMS):
neptune.append_tag(PARAMS['dataset_name'])
neptune.append_tag(PARAMS['model_name'])
K.clear_session()
tf.random.set_seed(34)
target_size = PARAMS['target_size']
BATCH_SIZE = PARAMS['BATCH_SIZE']
train_dataset, validation_dataset, info = create_Imagenette_dataset(
BATCH_SIZE,
target_size=target_size,
augment_train=PARAMS['augment_train'])
num_classes = info.features['label'].num_classes
encoder = base_dataset.LabelEncoder(info.features['label'].names)
train_dataset = train_dataset.map(
lambda x, y: apply_preprocess(x, y, num_classes),
num_parallel_calls=-1)
validation_dataset = validation_dataset.map(
lambda x, y: apply_preprocess(x, y, num_classes),
num_parallel_calls=-1)
PARAMS['num_classes'] = num_classes
steps_per_epoch = info.splits['train'].num_examples // BATCH_SIZE
validation_steps = info.splits['validation'].num_examples // BATCH_SIZE
neptune.set_property('num_classes', num_classes)
neptune.set_property('steps_per_epoch', steps_per_epoch)
neptune.set_property('validation_steps', validation_steps)
optimizer = tf.keras.optimizers.Adam(learning_rate=PARAMS['learning_rate'])
loss = 'categorical_crossentropy'
METRICS = ['accuracy']
base = tf.keras.applications.vgg16.VGG16(
weights='imagenet',
include_top=False,
input_tensor=Input(shape=(*target_size, 3)))
# TODO try freezing weights for input_shape != (224,224)
model = build_head(base, num_classes=num_classes)
model.compile(optimizer=optimizer, loss=loss, metrics=METRICS)
callbacks = [
neptune_logger,
ImageLoggerCallback(data=train_dataset,
freq=10,
max_images=-1,
name='train',
encoder=encoder),
ImageLoggerCallback(data=validation_dataset,
freq=10,
max_images=-1,
name='val',
encoder=encoder),
EarlyStopping(monitor='val_loss', patience=2, verbose=1)
]
model.summary(print_fn=lambda x: neptune.log_text('model_summary', x))
pprint(PARAMS)
history = model.fit(train_dataset,
epochs=10,
callbacks=callbacks,
validation_data=validation_dataset,
shuffle=True,
initial_epoch=0,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps)
def train_model(params,device):
embeddings=None
if(params['bert_tokens']):
train,val,test=createDatasetSplit(params)
else:
train,val,test,vocab_own=createDatasetSplit(params)
params['embed_size']=vocab_own.embeddings.shape[1]
params['vocab_size']=vocab_own.embeddings.shape[0]
embeddings=vocab_own.embeddings
if(params['auto_weights']):
y_test = [ele[2] for ele in test]
# print(y_test)
encoder = LabelEncoder()
encoder.classes_ = np.load(params['class_names'],allow_pickle=True)
params['weights']=class_weight.compute_class_weight('balanced',np.unique(y_test),y_test).astype('float32')
#params['weights']=np.array([len(y_test)/y_test.count(encoder.classes_[0]),len(y_test)/y_test.count(encoder.classes_[1]),len(y_test)/y_test.count(encoder.classes_[2])]).astype('float32')
print(params['weights'])
train_dataloader =combine_features(train,params,is_train=True)
validation_dataloader=combine_features(val,params,is_train=False)
test_dataloader=combine_features(test,params,is_train=False)
model=select_model(params,embeddings)
if(params["device"]=='cuda'):
model.cuda()
optimizer = AdamW(model.parameters(),
lr = params['learning_rate'], # args.learning_rate - default is 5e-5, our notebook had 2e-5
eps = params['epsilon'] # args.adam_epsilon - default is 1e-8.
)
# Number of training epochs (authors recommend between 2 and 4)
# Total number of training steps is number of batches * number of epochs.
total_steps = len(train_dataloader) * params['epochs']
# Create the learning rate scheduler.
if(params['bert_tokens']):
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps = int(total_steps/10), num_training_steps = total_steps)
# Set the seed value all over the place to make this reproducible.
fix_the_random(seed_val = params['random_seed'])
# Store the average loss after each epoch so we can plot them.
loss_values = []
if(params['bert_tokens']):
bert_model = params['path_files']
name_one=bert_model
else:
name_one=params['model_name']
if(params['logging']=='neptune'):
neptune.create_experiment(name_one,params=params,send_hardware_metrics=False,run_monitoring_thread=False)
neptune.append_tag(name_one)
if(params['best_params']):
neptune.append_tag('AAAI final best')
else:
neptune.append_tag('AAAI final')
best_val_fscore=0
best_test_fscore=0
best_val_roc_auc=0
best_test_roc_auc=0
best_val_precision=0
best_test_precision=0
best_val_recall=0
best_test_recall=0
for epoch_i in range(0, params['epochs']):
print("")
print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, params['epochs']))
print('Training...')
# Measure how long the training epoch takes.
t0 = time.time()
# Reset the total loss for this epoch.
total_loss = 0
model.train()
# For each batch of training data...
for step, batch in tqdm(enumerate(train_dataloader)):
# Progress update every 40 batches.
if step % 40 == 0 and not step == 0:
# Calculate elapsed time in minutes.
elapsed = format_time(time.time() - t0)
# `batch` contains three pytorch tensors:
# [0]: input ids
# [1]: attention vals
# [2]: attention mask
# [3]: labels
b_input_ids = batch[0].to(device)
b_att_val = batch[1].to(device)
b_input_mask = batch[2].to(device)
b_labels = batch[3].to(device)
# (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
model.zero_grad()
outputs = model(b_input_ids,
attention_vals=b_att_val,
attention_mask=b_input_mask,
labels=b_labels,
device=device)
# The call to `model` always returns a tuple, so we need to pull the
# loss value out of the tuple.
loss = outputs[0]
if(params['logging']=='neptune'):
neptune.log_metric('batch_loss',loss.item())
# Accumulate the training loss over all of the batches so that we can
# calculate the average loss at the end. `loss` is a Tensor containing a
# single value; the `.item()` function just returns the Python value
# from the tensor.
total_loss += loss.item()
# Perform a backward pass to calculate the gradients.
loss.backward()
# Clip the norm of the gradients to 1.0.
# This is to help prevent the "exploding gradients" problem.
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# Update parameters and take a step using the computed gradient.
# The optimizer dictates the "update rule"--how the parameters are
# modified based on their gradients, the learning rate, etc.
optimizer.step()
# Update the learning rate.
if(params['bert_tokens']):
scheduler.step()
# Calculate the average loss over the training data.
avg_train_loss = total_loss / len(train_dataloader)
if(params['logging']=='neptune'):
neptune.log_metric('avg_train_loss',avg_train_loss)
else:
print('avg_train_loss',avg_train_loss)
# Store the loss value for plotting the learning curve.
loss_values.append(avg_train_loss)
train_fscore,train_accuracy,train_precision,train_recall,train_roc_auc,_=Eval_phase(params,'train',model,train_dataloader,device)
val_fscore,val_accuracy,val_precision,val_recall,val_roc_auc,_=Eval_phase(params,'val',model,validation_dataloader,device)
test_fscore,test_accuracy,test_precision,test_recall,test_roc_auc,logits_all_final=Eval_phase(params,'test',model,test_dataloader,device)
#Report the final accuracy for this validation run.
if(params['logging']=='neptune'):
neptune.log_metric('test_fscore',test_fscore)
neptune.log_metric('test_accuracy',test_accuracy)
neptune.log_metric('test_precision',test_precision)
neptune.log_metric('test_recall',test_recall)
neptune.log_metric('test_rocauc',test_roc_auc)
neptune.log_metric('val_fscore',val_fscore)
neptune.log_metric('val_accuracy',val_accuracy)
neptune.log_metric('val_precision',val_precision)
neptune.log_metric('val_recall',val_recall)
neptune.log_metric('val_rocauc',val_roc_auc)
neptune.log_metric('train_fscore',train_fscore)
neptune.log_metric('train_accuracy',train_accuracy)
neptune.log_metric('train_precision',train_precision)
neptune.log_metric('train_recall',train_recall)
neptune.log_metric('train_rocauc',train_roc_auc)
if(val_fscore > best_val_fscore):
print(val_fscore,best_val_fscore)
best_val_fscore=val_fscore
best_test_fscore=test_fscore
best_val_roc_auc = val_roc_auc
best_test_roc_auc = test_roc_auc
best_val_precision = val_precision
best_test_precision = test_precision
best_val_recall = val_recall
best_test_recall = test_recall
if(params['bert_tokens']):
print('Loading BERT tokenizer...')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=False)
save_bert_model(model,tokenizer,params)
else:
print("Saving model")
save_normal_model(model,params)
if(params['logging']=='neptune'):
neptune.log_metric('best_val_fscore',best_val_fscore)
neptune.log_metric('best_test_fscore',best_test_fscore)
neptune.log_metric('best_val_rocauc',best_val_roc_auc)
neptune.log_metric('best_test_rocauc',best_test_roc_auc)
neptune.log_metric('best_val_precision',best_val_precision)
neptune.log_metric('best_test_precision',best_test_precision)
neptune.log_metric('best_val_recall',best_val_recall)
neptune.log_metric('best_test_recall',best_test_recall)
neptune.stop()
else:
print('best_val_fscore',best_val_fscore)
print('best_test_fscore',best_test_fscore)
print('best_val_rocauc',best_val_roc_auc)
print('best_test_rocauc',best_test_roc_auc)
print('best_val_precision',best_val_precision)
print('best_test_precision',best_test_precision)
print('best_val_recall',best_val_recall)
print('best_test_recall',best_test_recall)
# del model
# torch.cuda.empty_cache()
return model
def train_pnas(PARAMS):
ensure_dir_exists(PARAMS['log_dir'])
ensure_dir_exists(PARAMS['model_dir'])
neptune.append_tag(PARAMS['dataset_name'])
neptune.append_tag(PARAMS['model_name'])
neptune.append_tag(str(PARAMS['target_size']))
neptune.append_tag(PARAMS['num_channels'])
neptune.append_tag(PARAMS['color_mode'])
K.clear_session()
tf.random.set_seed(34)
train_dataset, validation_dataset, data_files = create_dataset(
dataset_name=PARAMS['dataset_name'],
batch_size=PARAMS['BATCH_SIZE'],
target_size=PARAMS['target_size'],
num_channels=PARAMS['num_channels'],
color_mode=PARAMS['color_mode'],
splits=PARAMS['splits'],
augment_train=PARAMS['augment_train'],
aug_prob=PARAMS['aug_prob'])
PARAMS['num_classes'] = data_files.num_classes
PARAMS['splits_size'] = {'train': {}, 'validation': {}}
PARAMS['splits_size'][
'train'] = data_files.num_samples * PARAMS['splits']['train']
PARAMS['splits_size'][
'validation'] = data_files.num_samples * PARAMS['splits']['validation']
steps_per_epoch = PARAMS['splits_size']['train'] // PARAMS['BATCH_SIZE']
validation_steps = PARAMS['splits_size']['validation'] // PARAMS[
'BATCH_SIZE']
neptune.set_property('num_classes', PARAMS['num_classes'])
neptune.set_property('steps_per_epoch', steps_per_epoch)
neptune.set_property('validation_steps', validation_steps)
encoder = base_dataset.LabelEncoder(data_files.classes)
# train_dataset = train_dataset.map(lambda x,y: apply_preprocess(x,y,PARAMS['num_classes']),num_parallel_calls=-1)
# validation_dataset = validation_dataset.map(lambda x,y: apply_preprocess(x,y,PARAMS['num_classes']),num_parallel_calls=-1)
# METRICS = ['accuracy']
callbacks = [
neptune_logger,
ImageLoggerCallback(data=train_dataset,
freq=10,
max_images=-1,
name='train',
encoder=encoder),
ImageLoggerCallback(data=validation_dataset,
freq=10,
max_images=-1,
name='val',
encoder=encoder),
EarlyStopping(monitor='val_loss', patience=25, verbose=1)
]
PARAMS['base_learning_rate'] = PARAMS['lr']
PARAMS['input_shape'] = (*PARAMS['target_size'], PARAMS['num_channels'])
model = build_model(PARAMS)
# if PARAMS['optimizer']=='Adam':
# optimizer = tf.keras.optimizers.Adam(learning_rate=PARAMS['lr'])
# base = tf.keras.applications.vgg16.VGG16(weights='imagenet',
# include_top=False,
# input_tensor=Input(shape=(*PARAMS['target_size'],3)))
# model = build_head(base, num_classes=PARAMS['num_classes'])
# model.compile(optimizer=optimizer,
# loss=PARAMS['loss'],
# metrics=METRICS)
model.summary(print_fn=lambda x: neptune.log_text('model_summary', x))
pprint(PARAMS)
history = model.fit(train_dataset,
epochs=PARAMS['num_epochs'],
callbacks=callbacks,
validation_data=validation_dataset,
shuffle=True,
initial_epoch=0,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps)
for k, v in PARAMS.items():
neptune.set_property(str(k), str(v))
return history
Epoch:\t{}\n\
Rounds:\t{}\n\
Total Number of Users:\t{}\n\
Selected Users:\t{}\n\
Server Pure Data: {}\n\
Mode:\t{}\n\
Attack:\t{}\n\
Attackers:\t{}\n\
Output folder:\t{}".format(args.epochs, args.rounds,
args.total_users_num,
args.selected_users_num, args.server_pure,
args.mode, args.attack_type,
args.attackers_num, args.log_dir))
# Neptune logging initialization
if args.neptune_log:
neptune.init(project_qualified_name=configs['log']['neptune_init'],
api_token=utils.get_neptune_token())
neptune.create_experiment(name=configs['log']['neptune_exp'],
upload_stdout=False,
upload_stderr=False)
neptune.append_tag(args.log_dir.split("/")[1])
last_round = main(arguments['--start-round'])
if args.neptune_log:
neptune.stop()
if last_round < args.rounds:
exit(1)
else:
exit(0)
def Eval_phase(params, which_files='test', model=None):
# For english, there is no translation, hence use full dataset.
if (params['language'] == 'English'):
params['csv_file'] = '*_full.csv'
# Load the files to test on
if (which_files == 'train'):
path = params['files'] + '/train/' + params['csv_file']
test_files = glob.glob(path)
if (which_files == 'val'):
path = params['files'] + '/val/' + params['csv_file']
test_files = glob.glob(path)
if (which_files == 'test'):
path = params['files'] + '/test/' + params['csv_file']
test_files = glob.glob(path)
'''Testing phase of the model'''
print('Loading BERT tokenizer...')
# Load bert tokenizer
tokenizer = BertTokenizer.from_pretrained(params['path_files'],
do_lower_case=False)
# If model is passed, then use the given model. Else load the model from the saved location
# Put the model in evaluation mode--the dropout layers behave differently
# during evaluation.
if (params['is_model'] == True):
print("model previously passed")
model.eval()
else:
model = select_model(params['what_bert'], params['path_files'],
params['weights'])
model.cuda()
model.eval()
# Load the dataset
df_test = data_collector(test_files, params, False)
if (params['csv_file'] == '*_translated.csv'):
sentences_test = df_test.translated.values
elif (params['csv_file'] == '*_full.csv'):
sentences_test = df_test.text.values
labels_test = df_test.label.values
# Encode the dataset using the tokenizer
input_test_ids, att_masks_test = combine_features(sentences_test,
tokenizer,
params['max_length'])
test_dataloader = return_dataloader(input_test_ids,
labels_test,
att_masks_test,
batch_size=params['batch_size'],
is_train=False)
print("Running eval on ", which_files, "...")
t0 = time.time()
# Tracking variables
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
true_labels = []
pred_labels = []
for batch in test_dataloader:
# Add batch to GPU
batch = tuple(t.to(device) for t in batch)
# Unpack the inputs from our dataloader
b_input_ids, b_input_mask, b_labels = batch
# Telling the model not to compute or store gradients, saving memory and
# speeding up validation
with torch.no_grad():
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
logits = outputs[0]
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# Calculate the accuracy for this batch of test sentences.
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
# Accumulate the total accuracy.
eval_accuracy += tmp_eval_accuracy
pred_labels += list(np.argmax(logits, axis=1).flatten())
true_labels += list(label_ids.flatten())
# Track the number of batches
nb_eval_steps += 1
# Get the accuracy and macro f1 scores
testf1 = f1_score(true_labels, pred_labels, average='macro')
testacc = accuracy_score(true_labels, pred_labels)
# Log the metrics obtained
if (params['logging'] != 'neptune' or params['is_model'] == True):
# Report the final accuracy for this validation run.
print(" Accuracy: {0:.2f}".format(testacc))
print(" Fscore: {0:.2f}".format(testf1))
print(" Test took: {:}".format(format_time(time.time() - t0)))
else:
bert_model = params['path_files'][:-1]
language = params['language']
name_one = bert_model + "_" + language
neptune.create_experiment(name_one,
params=params,
send_hardware_metrics=False,
run_monitoring_thread=False)
neptune.append_tag(bert_model)
neptune.append_tag(language)
neptune.append_tag('test')
neptune.log_metric('test_f1score', testf1)
neptune.log_metric('test_accuracy', testacc)
neptune.stop()
return testf1, testacc
def train_pyleaves_dataset(PARAMS):
ensure_dir_exists(PARAMS['log_dir'])
ensure_dir_exists(PARAMS['model_dir'])
neptune.append_tag(PARAMS['dataset_name'])
neptune.append_tag(PARAMS['model_name'])
neptune.append_tag(str(PARAMS['target_size']))
neptune.append_tag(PARAMS['num_channels'])
neptune.append_tag(PARAMS['color_mode'])
K.clear_session()
tf.random.set_seed(PARAMS['seed'])
train_dataset, validation_dataset, STAGE1_data_files, excluded = create_dataset(
dataset_name=PARAMS['dataset_name'],
threshold=PARAMS['threshold'],
batch_size=PARAMS['BATCH_SIZE'],
buffer_size=PARAMS['buffer_size'],
exclude_classes=PARAMS['exclude_classes'],
target_size=PARAMS['target_size'],
num_channels=PARAMS['num_channels'],
color_mode=PARAMS['color_mode'],
splits=PARAMS['splits'],
augmentations=PARAMS['augmentations'],
seed=PARAMS['seed'],
use_tfrecords=PARAMS['use_tfrecords'],
tfrecord_dir=PARAMS['tfrecord_dir'],
samples_per_shard=PARAMS['samples_per_shard'])
PARAMS['num_classes'] = STAGE1_data_files.num_classes
PARAMS['splits_size'] = {'train': {}, 'validation': {}}
PARAMS['splits_size']['train'] = int(STAGE1_data_files.num_samples *
PARAMS['splits']['train'])
PARAMS['splits_size']['validation'] = int(STAGE1_data_files.num_samples *
PARAMS['splits']['validation'])
PARAMS['steps_per_epoch'] = PARAMS['splits_size']['train'] // PARAMS[
'BATCH_SIZE']
PARAMS['validation_steps'] = PARAMS['splits_size']['validation'] // PARAMS[
'BATCH_SIZE']
neptune.set_property('num_classes', PARAMS['num_classes'])
neptune.set_property('steps_per_epoch', PARAMS['steps_per_epoch'])
neptune.set_property('validation_steps', PARAMS['validation_steps'])
# TODO: log encoder contents as dict
encoder = base_dataset.LabelEncoder(STAGE1_data_files.classes)
PARAMS['base_learning_rate'] = PARAMS['lr']
PARAMS['input_shape'] = (*PARAMS['target_size'], PARAMS['num_channels'])
# strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
# with strategy.scope():
model = build_model(PARAMS)
# model = build_or_restore_model(PARAMS)
model.summary(print_fn=lambda x: neptune.log_text('model_summary', x))
pprint(PARAMS)
backup_callback = BackupAndRestore(PARAMS['checkpoints_path'])
backup_callback.set_model(model)
callbacks = [
neptune_logger, backup_callback,
EarlyStopping(monitor='val_loss',
patience=25,
verbose=1,
restore_best_weights=True)
] #,
# ImageLoggerCallback(data=train_dataset, freq=1000, max_images=-1, name='train', encoder=encoder),
# ImageLoggerCallback(data=validation_dataset, freq=1000, max_images=-1, name='val', encoder=encoder),
history = model.fit(train_dataset,
epochs=PARAMS['num_epochs'],
callbacks=callbacks,
validation_data=validation_dataset,
shuffle=True,
steps_per_epoch=PARAMS['steps_per_epoch'],
validation_steps=PARAMS['validation_steps'])
# initial_epoch=0,
# TODO: Change build_model to build_or_load_model
model.save(PARAMS['saved_model_path'] + '-stage 1')
for k, v in PARAMS.items():
neptune.set_property(str(k), str(v))
if PARAMS['transfer_to_PNAS'] or PARAMS['transfer_to_Fossil']:
PARAMS['include_classes'] = STAGE1_data_files.classes
train_dataset, validation_dataset, STAGE2_data_files, STAGE2_excluded = create_dataset(
dataset_name=PARAMS['stage_2']
['dataset_name'], #PARAMS['dataset_name'],
threshold=PARAMS['threshold'],
batch_size=PARAMS['BATCH_SIZE'],
buffer_size=PARAMS['buffer_size'],
exclude_classes=PARAMS['exclude_classes'],
include_classes=PARAMS['include_classes'],
target_size=PARAMS['target_size'],
num_channels=PARAMS['num_channels'],
color_mode=PARAMS['color_mode'],
splits=PARAMS['splits'],
augmentations=PARAMS['augmentations'],
seed=PARAMS['seed'])
PARAMS['num_classes'] = STAGE2_data_files.num_classes
PARAMS['splits_size'] = {'train': {}, 'validation': {}}
PARAMS['splits_size']['train'] = int(STAGE2_data_files.num_samples *
PARAMS['splits']['train'])
PARAMS['splits_size']['validation'] = int(
STAGE2_data_files.num_samples * PARAMS['splits']['validation'])
PARAMS['steps_per_epoch'] = PARAMS['splits_size']['train'] // PARAMS[
'BATCH_SIZE']
PARAMS['validation_steps'] = PARAMS['splits_size'][
'validation'] // PARAMS['BATCH_SIZE']
backup_callback = BackupAndRestore(PARAMS['checkpoints_path'])
backup_callback.set_model(model)
callbacks = [
neptune_logger, backup_callback,
EarlyStopping(monitor='val_loss',
patience=25,
verbose=1,
restore_best_weights=True)
] #,
history = model.fit(train_dataset,
epochs=PARAMS['num_epochs'],
callbacks=callbacks,
validation_data=validation_dataset,
shuffle=True,
steps_per_epoch=PARAMS['steps_per_epoch'],
validation_steps=PARAMS['validation_steps'])
return history
parser.add_argument("--num_jobs", type=int, default=8)
parser.add_argument("--record_filtered", action="store_true")
parser.add_argument("--use_atomrings", action="store_true")
args = parser.parse_args()
args.algorithm = "gegl_constrained"
random.seed(0)
device = torch.device(0)
neptune.init(
project_qualified_name="sungsoo.ahn/deep-molecular-optimization")
experiment = neptune.create_experiment(name=args.algorithm,
params=vars(args))
neptune.append_tag(
f"{args.smi_id_min:03d}_{args.smi_id_max:03d}_{args.similarity_threshold}"
.replace(".", ""))
char_dict = SmilesCharDictionary(dataset=args.dataset,
max_smi_len=args.max_smiles_length)
dataset = load_dataset(char_dict=char_dict, smi_path=args.dataset_path)
if args.use_atomrings:
similarity_constrained_penalized_logp = similarity_constrained_penalized_logp_atomrings
else:
similarity_constrained_penalized_logp = similarity_constrained_penalized_logp_cyclebasis
for smi_id in range(args.smi_id_min, args.smi_id_max):
print(f"ID: {smi_id}")
reference_smi = dataset[smi_id]
benchmark = similarity_constrained_penalized_logp(
import neptune
neptune.init(
project_qualified_name=
'shared/onboarding', # change this to your `workspace_name/project_name`
api_token='ANONYMOUS', # change this to your api token
)
# Step 3: Create an experiment and save parameters
neptune.create_experiment(name='great-idea', params=params)
# Step 4. Add tags to organize things
neptune.append_tag(['experiment-organization', 'me'])
# Step 5. Add logging of train and evaluation metrics
neptune.log_metric('train_f1', train_f1)
neptune.log_metric('test_f1', test_f1)
# Step 6. Run a few experiments with different parameters
# tests
current_exp = neptune.get_experiment()
correct_logs = ['train_f1', 'test_f1']
if set(current_exp.get_logs().keys()) != set(correct_logs):
raise ValueError()
# select project
neptune.init('USERNAME/example-project')
# define parameters
PARAMS = {'timeseries_factor': 1.7, 'n_iterations': 200, 'n_images': 7}
# create experiment
neptune.create_experiment(name='timeseries_example', params=PARAMS)
# log some metrics
for i in range(1, PARAMS['n_iterations']):
neptune.log_metric('iteration', i)
neptune.log_metric('timeseries',
PARAMS['timeseries_factor'] * np.cos(i / 10))
neptune.log_text('text_info', 'some value {}'.format(0.95 * i**2))
# log property (key:value pair)
neptune.set_property('timeseries_data_hash', '123e4567')
# add tag to the experiment
neptune.append_tag('timeseries_modeling')
# log some images
for j in range(PARAMS['n_images']):
array = np.random.rand(10, 10, 3) * 255
array = np.repeat(array, 30, 0)
array = np.repeat(array, 30, 1)
neptune.log_image('mosaics', array)
neptune.stop()